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Courses & Programmes
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Accounting
Course - Bachelor (University)Accounting is the language required to do business. It allows executives, managers and employees to communicate; it allows investors, analysts, and governmental authorities to understand how an organization is doing. Thanks to the International Financial Reporting Standards, accounting can be seen as a global language, spoken across most countries.
Accounting is much more than book-keeping. Without accounting, it is not possible to evaluate a new business opportunity, assess the performance of a company, or design a solid business strategy for the near future. This course provides a thorough introduction to financial and managerial accounting. It provides fundamental knowledge that all business practices require.
This course teaches how to formally record and report economic events and transactions, how to read accounting information to make inferences and support decisions, and how accounting plays an active role in the success or failure of a company. More broadly, this courses explains why the accounting activity cannot be performed by robots, as it involves discretion in how information is recorded and reported. Moreover, the course offers a first look at what the accounting profession entails.
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Advanced Econometrics
Course - MasterThis course covers both theoretical and practical aspects of complex dynamic econometric models that are used in the industry, by central banks, governments, think tanks, and other research institutes. The students will be introduced to stochastic theory that allows them to fully understand the dynamic properties of complex models featuring nonlinearities, time-varying parameters and latent variables. Important concepts include invertibility, stationarity, dependence, ergodicity and bounded moments.
The students will also be introduced to advanced estimation theory that allows them to “bring” state-of-the-art models to the data and conduct inference on parameters under very general conditions. Important topics include the existence, measurability, consistency and asymptotic normality of extremum, M and Z estimators. We also cover advanced topics in nonlinear model selection and specification, estimation and inference under incorrect specification and metric selection. From a practical perspective, the advanced methods and state-of-the-art models are used for forecasting and policy analysis in a wide number of applications ranging from finance to macroeconomics.
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Advanced Econometrics 1
Course - MasterThe aim of the Advanced Econometrics 1 course is to obtain a deep understanding of econometric theory, practice and inference using a variety of advanced econometric techniques.
After passing the course, students should be able to apply advanced econometric techniques in practice, to extend currently available methods when needed for particular applications, to implement these methods in a matrix programming environment, and to understand and derive their statistical properties.
Econometrics
Actuarial Science and Mathematical Finance
UvA -
Advanced Econometrics 2
Course - MasterThe aim of the Advanced Econometrics 2 course is to obtain a deep understanding of econometric theory, practice, and inference using four special advanced econometric topics. The contents of this course build upon the general knowledge acquired in the course Advanced Econometrics 1.
Topics include: bootstrap methods; semi- and non-parametric methods; weak identification; panel data models.
Econometrics
Actuarial Science and Mathematical Finance
UvA -
Advanced Linear Programming
Course - MasterThis course is part of the Mastermath programme. All information can be found at the Mastermath website.
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Advanced Machine Learning
Course - MasterMachine learning is the science of getting computers to act without being explicitly programmed. Machine learning is so pervasive today that it is used in everyday life without knowing it. In this course, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work yourself. We will discuss the theoretical underpinnings as well as the practical know-how needed to apply these techniques to new problems.
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Advanced Network Programming
Course - Bachelor (University)The objective of this course is to teach students about advanced
concepts in networking technologies beyond the basic TCP/IP protocol and
socket abstraction. Upon completion of this course students will be able
to:
* Understand the internals of Linux’s TCP/IP stack implementation, and
data center networks
* Explain network stack design and implementation challenges associated
with multi-core systems and high-speed networks (100+ Gbps)
* Analyze various network stack designs (kernel, userspace,
application-specific) and APIs (e.g., sockets, RDMA and friends) on
their trade-offs, performance gains, and implementation efforts
* Understand how to build a high-performance network infrastructure for
large-scale cloud data centers and how to program and manage such a
network with software
* Learn about ongoing and emerging research challenging in networking
technologies -
Advanced Networking
Course - MasterPlease refer to the System and Network Engineering web pages for detailed and current course information.
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Advanced Networking
Track - MasterPlease refer to the System and Network Engineering web pages for detailed and current course information.
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Advanced Operating Systems
Course - Bachelor (University)The course will feature a number of hands-on assignments accompanied by lectures on advanced operating system kernel design and programming concepts. In each assignment, students will be expected to start with a minimal kernel implementation and exercise their kernel hacking skills on one of the major operating subsystems (i.e., memory management, process management, drivers, etc.). This will involve programming in both C and assembly as well as directly interfacing with the hardware. The course will also link lectures and assignments to modern operating system features and offer insights into state-of-the-art OS research efforts.
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Advanced Programming
Course - Bachelor (University)To learn advanced programming skills, to get to know and understand advanced programming concepts like inheritance and to get experience with programming some of the data structures that were taught in the course Data Structures & Algorithms.
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Advanced Programming
Course - Bachelor (University)The student will have an appreciation of modern programming paradigms as realised in a range of current programming languages.
The student will have achieved an understanding of how programming languages are interpreted and compiled through the practical construction of interpreters.
The student will be able to quickly assess and acquire a novel programming language.Learning Outcomes
Students who have completed the course will have an understanding of the principles of programming languages and the methodological foundations of computer programming as well as the demonstrated ability to apply these principles. They will have highly-developed analysis and problem solving skills as well as the ability to quickly acquire and program in a range of programming languages. -
Advanced Research Methods and Statistics
Course - Bachelor (University)In this course we will cover a series of techniques that are more advanced than those covered in BRMS1, BRMS2, or Statistics for Sciences. We will work extensively with science and social science data and learn how to analyze and interpret data at an advanced level. The course covers the following topics:
– Review of multivariate linear regression and ANOVA
– Basic matrix algebra
– Model selection and diagnostics in multivariate linear regression
– Logistic regression
– (M)ANOVA and (M)ANCOVA
– Discriminant analysis and classification
– Principle component analysis
– Analysis of repeated measures
– Categorical data analysis and multi-way frequency tablesThe central aim of the course will be that students acquire the skills to conduct and interpret quantitative analyses of various empirical studies. Students will also analyze science and social science data using the techniques learned in the course and complete data analysis assignments.
Bachelor’s Liberal Arts and Sciences, major Sciences
Bachelor’s Liberal Arts and Sciences, major Social Sciences
Bachelor’s Liberal Arts and Sciences, Academic Core
UvAEnglish
AUC
8 weeks
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Advanced Security
Course - MasterPlease refer to the System and Network Engineering web pages for detailed and current course information.
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Advanced Statistics
Course - Bachelor (University)At the end of this module a student is able to:
• correctly reproduce the central concepts of multiple regression analysis;
• independently and correctly calculate statistics such as the (un)standardized b-coefficient, intercept, Sum or Squared Errors, Total Sum or Squares r, R², f and t values, and standard deviations;
• independently and correctly perform multiple regression analysis with interaction-effects and categorical independent variables with statistical software;
• independently and correctly interpret the results of statistical analysis performed by themselves and/or by statistical software;
• independently and in a correct, precise and clear way write up results of quantitative hypothesis-testing research regarding variability among different social groups. -
Advanced Statistics
Course - MasterAfter this course, the students will understand the basic principles of
multilevel analysis and longitudinal data analysis. Furthermore, they
will be able to perform these techniques with standard software
packages. In addition, they will understand the principles of open
science and the current debate about null hypothesis significance
testing.
Specific goals are that:
• The student is able to explain and apply multilevel analysis for
cross-sectional data
• The student is able to explain and apply the basic principles of the
advanced techniques for longitudinal data.
• The student is able to explain the differences between different
methods and models of analysing clustered data and to motivate a choice
for one of these models in the context of epidemiological datasets/
research examples.
• The student can interpret results from the various methods and models
in the context of epidemiological datasets/ research examples
• The student is capable of performing the advanced techniques using
Stata
• The student can explain the principles of open science and is able to
relate these to quantitative data analyses
• The student can explain pitfalls of null hypothesis significance
testing (NHST) and can draw conclusions without reliance on NHST
The student is capable of performing the advanced techniques using
Stata
• The student can deliver an oral presentation following a scientific
format on a data-analysis assignment involving correlated data focusing
on the data-analyses, results and conclusion.
• The student can write the data-analysis, results and conclusion
section of a short scientific paper demonstrating he/she is able to
reflect on the results of the advanced analyzing techniques -
Advanced Statistics for Analytical Chemistry
Course - MasterIn this course you will be made familiar with the main tools in the chemometrics toolbox, PCA, PLS, PLSDA and how to apply them in various fields and applications. Besides these tools we will look at different properties of spectroscopic and MS data and their preprocessing methods. Finally, validation of the multivariate models gets sufficient attention.
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Algorithms and Data Structures in Python
Course - Bachelor (University)This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms and data structures used to solve these problems. It also uses the Python programming language to implement and test algorithms and data structures on realistic datasets. The technological topics which will be covered in this course are:
- Python Programming Basics;
- Introduction to Object-Oriented Programming in Python;
- Algorithm Analysis;
- Basic Data Structures:
- Recursion;
- Sorting and Searching;
- Trees and Tree Algorithms;
- Graphs and Graph Algorithms.
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Algorithms in Sequence Analysis
Course - MasterHave you ever wondered how we can track a gene across 3 billion years of evolution? Sequence alignment can be used to compare genes from humans and bacteria, using a dynamic programming algorithm. In this course we focus on algorithms for biological sequences that can be applied to real scientific problems in biology.
Students will gain in depth knowledge on the theory of sequence analysis methods. They will also develop understanding and skills to apply the algorithms to protein and DNA sequences. We would like to stress that no biological knowledge is required to enter this course.
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Amsterdam Data Science & Artificial Intelligence Minor
Minor - Bachelor (University)What is Data Science? Data Science & Artificial Intelligence is about discovering hidden patterns in large amount of data, using computers, brainpower and the wealth of data. These days, many innovations heavily rely on Data Science & Artificial Intelligence. Think about:
- self-driving cars;
- social network analysis;
- teaching computers to understand text;
- image and face recognition;
- targeted advertising;
- sport analytics.
Main objective of this minor is to learn you to carry out a Data Science & Artificial Intelligence project from the beginning to the end.
To increase your success during the minor, basic Python and Statistics knowledge will be required. To obtain this knowledge or to fresh it up we offer online courses. Consult ‘Registration and entry requirements’ for more information on these courses.
What will you learn in this Minor? You will learn to carry out a Data Science project from beginning to end. That involves: Asking the right (research) question(s), data collection and cleaning, data management and organisation, modelling, communication and visualisation, and implementation.
Your Future: This Minor will give you access to both UvA and VU Master programmes: Information Studies: The ‘Data Science’ track teaches you to apply Data Science in a wide range of areas; Business Administration: The ‘Digital Business’ track teaches you to apply Data Science in a commercial/business context (additional requirements also apply). This Minor will give you excellent job prospects. McKinsey have projected a shortfall of 190,000 Data Scientists by 2018!
Entry requirements: Successful completion of the first year of a Bachelor is required for participation in this Minor. In addition, the following knowledge is assumed: Basic Python & Statistics knowledge (if this is lacking self-study is required). Students taking this Minor typically follow a Bachelor in: Actuarial Sciences, Business, Communication Sciences, Econometrics, Economics, Psychology, STEM studies (Science, Technology, Engineering, Mathematics). The Minor is also open to other students, provided they have an affinity with quantitative methods.
Curriculum: The Minor is comprised of the following courses (more information in the course catalogue): Databases & Data Visualisation; Data Structures with Python; Data Wrangling; Ethics & Law; Machine Learning; Text Retrieval & Mining.
Further information:
- www.uva.nl/minors
- www.schoolofdatascience.amsterdam/education/amsterdam-data-science-minor
- info@schoolofdatascience.amsterdam
- See the flyer
Network:
The Data Science Minor is part of the Amsterdam School of Data Science, an initiative of Amsterdam Data Science, which organises Meet-ups also open to students:
In brief:
- Name: Amsterdam Data Science Minor
- Minor Credits: 30 EC
- Duration: 6 months
- Start: September
- Language: English
- Entry requirements: See section
- Minimum: 30 students; Maximum: 75
Many e.g. Actuarial Sciences, Business, Communication Science, Econometrics, Economics, Psychology, STEM studies (Science, Technology, Engineering, Mathematics).
Amsterdam Data ScienceEnglish
6 months
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Amsterdam Leadership Programme
Course - Professional educationAfter this course students will be able to:
– Develop insights into personal strengths, weaknesses, core values and development priorities;
– Develop the ability to inquire and advocate in an effective way;
– Understand and apply different styles of influencing with integrity;
– Reflect on the effectiveness of one’s leadership behaviours by applying practical concepts;
– Develop insights into importance of diversity and inclusiveness in leadership;
– Create a culture of learning and giving/receiving high quality feedback;
– Generate an effective team charter in order to maximise the impact of team work.
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Analytics Translator
Programme - Professional educationIn this 4-day interactive course provided by the Amsterdam Business School you will learn key skills to perform as an Analytics Translator. You will play the bridging role between the technical expertise of data scientists and the operational expertise of domains such as marketing, HR, supply chain, finance. This role is crucial to ensure that the data science efforts connect flawlessly to the business needs. This unique course will prepare you for this new and valuable role in every organisation. The next edition will start on Thursday 15 April 2021.
What you will learn:
- A fundamental understanding of machine learning methods.
- Identifying business opportunities for data science solutions.
- Essential statistical concepts.
- Data visualisation, dashboard design and storytelling with data.
- Implementation of data science projects.
- How to translate between data science team and business team/management.
- Data science ethics and regulations, including GDPR.
- Understand data science roles and what kind of teams are needed.
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Analytics Translator
Course - Professional educationIn this four day interactive course you will learn key skills to perform as an Analytics Translator. You will play the bridging role between the technical expertise of data scientists and the operational expertise of domains such as marketing, HR, supply chain, finance. This role is crucial to ensure that the data science efforts connect flawlessly to the business needs.
This course is designed for business professionals who want to become the crucial link between business and data science and analytics teams. The profile of the participants will be business professionals in finance, auditing, control, risk, marketing, HR, sales, logistics, supply chain, etc.
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Analytics Translator – online programme
Programme - Professional educationIn this interactive online programme provided by the Amsterdam Business School, you will learn how to connect the technical expertise of data scientists to the operational expertise of domains such as marketing, HR and finance. The programme consists of 2 modules: Machine Learning for Analytics Translators (start: Tuesday 27 October) and Data Science in Business for Analytics Translators (start: Thursday 12 November). Choose to follow the entire programme or pick one of the modules, whatever fits your needs best.
This online programme consists of 2 modules. You can choose to follow 1 or both modules.
Module 1: Machine Learning for Analytics Translators
As an analytics translator, you have some knowledge of machine learning and data science, but not to the technical depth at which data scientists do their work. You are uniquely able to approach the project from the viewpoint of the business. This module consists of 4 half-days in which you will gain an understanding of statistics and machine learning. This knowledge will allow you to spot opportunities for techniques that can be used to solve a variety of business problems. No programming skills are needed to follow this module.
Module 2: Data Science in Business for Analytics Translators
This module consists of 6 half-days, in which you will learn how you can guide your business through a digital transformation with data science solutions. You will gain an understanding of what it means to write a data science project plan that fits your audience. This will allow you to facilitate a successful implementation of data science techniques within your business. Statistics and machine learning basics are recommended for this module.
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Applied Analysis: Financial Mathematics
Course - MasterThis course introduces you to the maths used within finance and financial institutions.
Topics covered include the theory of options, binomial method, Black-Scholes model and its application, heat equation, and numerical methods.
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Applied Econometrics
Course - MasterThis course is about regression analysis, which in applied economics is a powerful tool to analyse empirical relationships. First, estimation and testing of the basic linear regression model by ordinary least squares (OLS) and instrumental variables (IV) will be reviewed. Particular attention will be paid to the statistical assumptions underlying the basic model. These assumptions should be valid in applications in order to give reliable outcomes. Second, we will focus on various applications and extensions of the basic model.
We will cover the analysis of: (1) experimental data, (2) panel data and (3) time series.
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Applied Econometrics for Business
Course - MasterThe objective of this course is to give Business students the necessary econometric tools to complete te Finance specialisation; Finance is a field with strong emphasis on empirical work and rich datasets. Upon successful completion of this course students should have the following knowledge, skills and attitudes:
- a solid knowledge of the various types of datasets used in Empirical Finance, and their corresponding techniques (time-series, cross-sectional, event-study and panels);
- a solid understanding of the assumptions underlying regression analysis;
- a solid knowledge of the statistical tests and techniques used to detect and remedy violations of these assumptions;
- a solid understanding of the factors influencing the significance found in empirical research;
- be able to conduct statistical tests on regression results in order to answer research questions.
All of this will have a strong focus on applied skills.
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Applied Econometrics for Spatial Economics
Course - MasterPublic policies need to be evaluated in order to understand their
effectiveness and correct validation of economic theory can only be
achieved with empirical research. The main objective of this course is
to provide an overview of econometric research methods in urban, real
estate, transport and environmental economics and to teach you how to
apply these methods to real-world data. After following this course, you
will:
• have an advanced understanding of the mathematical and statistical
concepts underlying regression analyses in spatial economics;
• understand the importance of and difficulties in estimating causal
effects as opposed to correlations in spatial economic problems;
• know how to appropriately interpret regression results of various
estimators and know which one to apply in particular situations,
depending on (i) the nature of the data (cross-sectional / panel /
discrete data) and (ii) the task at hand (i.e., valuation of public
policies, testing of economic theories or estimating parameters as
derived from theory);
• understand and know how to apply techniques that are commonly in use
in urban, real estate, transport and environmental economics and policy:
spatial econometrics, discrete choice models and quasi-experimental
research designs;
• be able to apply these methods independently to typical datasets in
spatial economics using the software package STATA. -
Applied Econometrics for Urban, Transport and Environmental Economics
Course - Bachelor (University)Public policies need to be evaluated in order to understand their effectiveness and correct validation of economic theory can only be achieved with empirical research. The main objective of this course is to provide an overview of econometric research methods in urban, transport and environmental economics and to teach you how to apply these methods to real-world data. After following this course, you will:
• have an advanced understanding of the mathematical and statistical concepts underlying regression analyses in spatial economics;
• understand the importance of and difficulties in estimating causal effects as opposed to correlations in spatial economics problems;
• know how to appropriately interpret regression results of various estimators and know which one to apply in particular situations, depending on (i) the nature of the data (cross-sectional / panel /discrete data) and (ii) the task at hand (i.e., valuation of public policies, testing of economic theories or estimating parameters as derived from theory);
• understand and know how to apply techniques that are commonly in use in urban, transport and environmental economics and policy: spatialeconometrics, discrete choice models and quasi-experimental research designs;
• be able to apply these methods independently to typical datasets in spatial economics using the software package STATA. -
Applied Financial Econometrics
Course - MasterThis course is about regression analysis, which in empirical finance is a powerful tool to analyse empirical relationships.
First, estimation and testing of the basic linear regression model by ordinary least squares (OLS) and instrumental variables (IV) will be reviewed. Particular attention will be paid to the statistical assumptions underlying the basic model. These assumptions should be valid in applications in order to give reliable outcomes. Second, we will focus on various applications and extensions of the basic model. We will cover the analysis of:- experimental data;
- panel data;
- time series.
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Applied Machine Learning
Course - MasterMachine Learning is marking a revolution in the world. Originally an academic research topic, the last decade has seen a major paradigm shift with Machine Learning used in many companies for a wide range of services. From deleting SPAM mail from your inbox to ranking the Google search results, and from defining your Facebook stream to serving you the advertisement on a website.
In the Applied Machine Learning course we study and learn from large collections of unstructured data, such as text documents, web pages, images and videos. We address the complete machine learning chain, from designing the system and its objectives, to representing data and selecting and evaluating the learning method. We review and focus on the foundations of retrieval, supervised classification and unsupervised clustering. You will learn the theoretical concepts during the lectures with a keen eye on the design of the full learning system.
In the tutorials we will focus on some of the important mathematical concepts, and in the lab you will gain hands-on experience through a number of coding assignments and by participating in a Kaggle competition. Finally, a few experts from the field (both academic as well as industry colleagues) are invited to give guest lectures.
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Applied Statistics
Course - MasterThis course introduces the basic concepts underlying applied statistics.
Its main focus is on estimation of effect sizes and confidence
intervals, and elementary statistical tests. The applied techniques that
will be introduced are aimed at description of observed data, and
estimation of and testing null-hypotheses about single population means
and proportions, differences between means and proportions, contrast
analysis of more than two means in independent and repeated measures
designs, and correlation. Tests that will be introduced are the t-test,
chi-square test, and the ANOVA F-test. -
Applied Stochastic Modeling
Course - Bachelor (University)This course deals with a number of stochastic modeling techniques that are often used in practice. They are motivated by showing the business context in which they are used. Topics we deal with are: time-dependent Poisson processes and infinite-server queues, renewal processes and simulation, birth-death-processes, basic queueing models, and inventory models. We also repeat and extend certain parts of probability theory.
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Applied Stochastic Modelling
Course - MasterThe Applied Stochastic Modelling course provides you with an insight into mathematical modelling and the way it is used in practice. You will explore a number of stochastic solution methods.
Topics that are dealt with are: birth-death-processes, basic queueing models, inventory models, renewal theory and simulation. We also repeat and extend certain parts of probability theory.
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Architectuur en Computerorganisatie
Course - Bachelor (University)The development of modern computer technology requires professionals with a background in all science fields, who understand both hardware and software. The interaction between the hardware and software on a variety of levels provides a framework for understanding the fundamentals of computing. Whether your primary interest is hardware or software, computer science or electrical engineering, the central ideas remain the same. This course will show the relationship between hardware and software, and focus on the concepts that are the basis for today’s computers.
This course is only available in Dutch.
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Archival and Information Studies
Programme - MasterArchival and Information Studies offers two different tracks: 60 EC track Information Studies and a 90 EC (dual) track Archival Studies.
The Information Studies track of the Master’s in Archival and Information Studies is a one-year master’s programme for students who are interested in the critical and practical examination of this new shifting informational landscape. The track is an interdisciplinary programme taught by, archivists, information scientists, cultural theorists, and heritage specialists. It treats contemporary information theory and practice, trains students for the diversity of professional information roles and provides insight into both the history of information technology and practice, as well as its future directions.
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Artificial Intelligence
Programme - Bachelor (University)Artificial Intelligence is about analysing and automating tasks that require intelligence. In other words, you teach machines to be as intelligent as possible. Computer systems to detect credit card fraud, or a telephone-based railway route planner which understands spoken language: these are just two examples of how artificial intelligence can be applied.
Computers are increasingly used to support people, in decision-making or in independently performing tasks requiring intelligence. Humans are an important source of inspiration for artificial intelligence. You cannot analyse and automate intelligence without understanding what human intelligence is; in other words, how people learn and reason. The Bachelor’s programme in Artificial Intelligence therefore focuses on cognitive psychology, logic, linguistics and philosophy.
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Artificial Intelligence in Amsterdam
Programme - MasterThe Artificial Intelligence (AI) Master’s programme in Amsterdam has a technical approach towards AI research. It is a joint programme of the University of Amsterdam and Vrije Universiteit Amsterdam. This collaboration guarantees a wide range of topics, all taught by world renowned researchers who are experts in their field. The primary focus is on the development and understanding of intelligent computational processes in order to create useful artefacts, as well as to aid in understanding (human) intelligence. In the programme, you acquire a working knowledge of efficient, robust and intelligent methods for interpreting sensory and other information from different modalities.
AI is a field that develops intelligent algorithms and machines. Examples include: self-driving cars, smart cameras, surveillance systems, robotic manufacturing, machine translations, internet searches, and product recommendations. Modern AI often involves self-learning systems that are trained on massive amounts of data (“Big Data“), and/or interacting intelligent agents that perform distributed reasoning and computation. AI connects sensors with algorithms and human-computer interfaces, and extends itself into large networks of devices. AI has found numerous applications in industry, government and society, and is one of the driving forces of today’s economy.
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Artificial Intelligence: Cognitive Science
Track - MasterImmerse yourself in the multidisciplinary study of mind and cognition. Researchers in Cognitive Science come from a wide range of backgrounds, including psychology, computer science, artificial intelligence, philosophy, mathematics and neuroscience. They all share the common goal of gaining a deeper understanding of the human mind, for both theoretical and practical purposes.
The track focusses on the processes that underlie human functioning from two different research perspectives: empirical work and computational modelling. The combination of these two perspectives allows for a better understanding of the mechanisms underlying human functioning.
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Artificial Intelligence: Socially Aware Computing
Programme - MasterArtificial Intelligence is widely adopted in our society: think of face recognition in your smart phone, decision support for medical doctors, or smart homes for assisted living. At VU, you can study AI technology from a societal perspective: how can we develop and evaluate computer-based technology that exploits knowledge about human functioning. There are two tracks: one focusing on how to interpret, support and adapt to human behaviour, another on understanding cognitive aspects of AI. The two tracks Socially Aware Computing and Cognitive Science are oriented towards the practical application of AI technology in a broad, problem-oriented setting. You’ll learn how to model both mental and physiological processes of human functioning. Such models are the basis of intelligent applications that support humans in their daily lives in a dedicated manner, also to enable that support systems to understand humans better.
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Asymptotic Statistics
Course - MasterThis course is part of the Mastermath programme. All information can be found at the Mastermath website.
Master’s Mathematics
Master’s Stochastics and Financial Mathematics
UvA -
B&C Methods and Statistics
Course - Bachelor (University)In this course we will explore ways in which a researcher can approach data. For example, we will explore the theory behind machine learning, its application and its limitations. This course provides theoretical knowledge and subsequently challenges the students to apply this knowledge practically.
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Basic Probability: Programming
Course - MasterThis course is designed to provide students with the background in discrete probability theory and programming that is necessary to follow other more advanced master-level courses in areas such as linguistics, natural language processing, machine learning, complexity theory, cryptography, information theory, quantum computing, combinatorics, etc. The goal is to make students that have had no prior exposure to probability theory and/or programming feel comfortable in these areas. To achieve this goal we will try to illustrate the theoretical concepts with real-life examples that relate to topics in, e.g., computer science, gambling, and the like. Moreover, we will make sure that there is a close tie between the theoretical and practical part of the course, thus enabling students to apply their newly acquired theoretical knowledge to real problems.
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Basic Probability: Theory
Course - MasterThis course is designed to provide students with the background in discrete probability theory and programming that is necessary to follow other more advanced master-level courses in areas such as linguistics, natural language processing, machine learning, complexity theory, cryptography, information theory, quantum computing, combinatorics, etc. The goal is to make students that have had no prior exposure to probability theory and/or programming feel comfortable in these areas. To achieve this goal we will try to illustrate the theoretical concepts with real-life examples that relate to topics in, e.g., computer science, gambling, and the like. Moreover, we will make sure that there is a close tie between the theoretical and practical part of the course, thus enabling students to apply their newly acquired theoretical knowledge to real problems.
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Basic Research Methods and Statistics I
Course - Bachelor (University)Social science is best characterized as an ongoing activity, namely the creation of new knowledge. This activity follows rather strict rules in order to gain acceptance by the scientific community.
This course covers all discipline-independent aspects of creating new knowledge:
• How to formulate a scientific question
• How to plan an investigation bearing on that question
• How to conduct the inquiry
• How to present the data that result from your research
• How to interpret your results, and extrapolate beyond your data
• How to report the results in an appropriate way
The central question we will address in this course is this: How do I conduct research such that it will yield conclusions that are acceptable to critical peers? Central concepts:
• The Empirical Cycle: start with a problem or question, conduct experiments / observe reality, interpret data in light of the original problem, form a tentative conclusion, make predictions.
• Experimental design: organize experiments / observations such that they allow unequivocal interpretations.
• Statistics: a branch of probability calculus that deals with data analysis. It aims to extrapolate the results of an investigation beyond the boundary of the sample by adding an element from outside the investigation: a mathematical description of the data. This addition allows for making quantitative judgments about the meaningfulness of the data.
• Communicating results: presenting findings using oral and written reports.
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Basic Research Methods and Statistics II
Course - Bachelor (University)The central aim of the course will be that students
acquire the skills and knowledge to conduct research,
and quantitatively analyze data. The central question we
will address in this course is: How do I design research
and analyze data such that it will yield conclusions that
are acceptable to critical peers? On one hand, this course
will further students’ quantitative skills developed in
BRMS I (1st year course); various quantitative methods
commonly used in social science research are covered.
On the other hand the steps in designing research
(formulating research questions, operationalizing,
planning and conducting an empirical study) will be
treated.
The course will consist of an alternating series of
interactive lectures and practicals in which students
learn the theoretical background as well as the
application of several statistical techniques, including:
correlation, reliability, multiple linear regression, and
ANOVA. The practicals will mostly be used to learn
coding, analyzing, reporting and interpreting data using
SPSS.
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Basic Skills in Mathematics, Programming & Statistics
Course - Bachelor (University)In order to become a skilled and versatile methodologist, you will need a rich methodological toolbox. But before acquiring those tools, in this first course of the first semester of the PML specialization, you will build the box itself. Boring you say? No! The box will give you the fundament for all the advanced tools you will learn during the remainder of the programme. You will build your box from the basics of mathematics, statistical programming, and statistics. The more solid these fundaments are, the fancier your box will be, and the better you’ll be able to build your statistical toolset during the subsequent courses.
Mathematics
In this module, you will improve your algebra skills. You will simplify and solve equations involving logarithmic and exponential functions. In addition, you will learn how to differentiate functions. These skills will help you to compute maxima and minima of functions. Finally, you will learn how to conduct matrix algebra.
Statistical programming
In this module, you will learn the programming language R (and thereby fully replace SPSS). We will focus on working with datasets, using and programming your own functions, plotting and basic programming routines (if-statements and loops).
Frequentist statistics
In this module, you will use R simulations to gain a conceptual and practical understanding of fundamental concepts in statistics. We will cover elementary probability theory (e.g., marginal, joint, and conditional probabilities), probability distributions (e.g., discrete and continuous distributions), hypothesis testing (e.g., central limit theorem, standard deviations, p-values, confidence intervals, power), and regression analysis (e.g., polynomial regression, model selection, overfitting).
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Bayesian Econometrics
Course - Bachelor (University)This course (named Computational Econometrics (E_EOR3_CE) in the past
academic years) will cover Bayesian statistics where the topics include
the prior and posterior density, Bayesian hypothesis testing, Bayesian
prediction, and Bayesian Model Averaging for forecast combination.
Several models will be considered, including the Bernoulli/binomial
distribution, the Poisson distribution and the normal distribution.
Obviously, attention will be paid to the Bayesian analysis of linear
regression models. Also simple time series models will be considered. An
important part of the courses is the treatment of simulation-based
methods such as Markov chain Monte Carlo (Gibbs sampling, data
augmentation, Metropolis-Hastings method) and Importance Sampling, that
are often needed to compute Bayesian estimates and predictions and to
perform Bayesian tests. -
Bayesian Econometrics for Business
Course - Bachelor (University)This course will cover Bayesian statistics where the topics include the prior and posterior density, Bayesian hypothesis testing, Bayesian prediction, and Bayesian Model Averaging for forecast combination. Several models will be considered, including the Bernoulli/binomial distribution, the Poisson distribution and the normal distribution. Obviously, attention will be paid to the Bayesian analysis of linear regression models. Also simple time series models will be considered. An important part of the courses is the treatment of simulation-based methods such as Markov chain Monte Carlo (Gibbs sampling, data augmentation, Metropolis-Hastings method) and Importance Sampling, that are often needed to compute Bayesian estimates and predictions and to
perform Bayesian tests. -
Bayesian Statistics
Course - Bachelor (University)Frequentistische statistiek is gebaseerd op de veronderstelling dat de data verdeeld is volgens een onbekende distributie. Bayesiaanse statistiek gaat uit van een ander beginsel, waarin data en parameter op gelijke voet behandeld worden. De Bayesiaanse procedure vereist naast specificatie van het model ook keuze van een zogeheten prior verdeling over het model. De data wordt ingezet om de prior om te zetten in de zogenaamde posterior verdeling. In dit vak kijken we naar enkele gangbare statistische vragen, zoals schatting van de model parameter, toetsing van hypotheses, constructie van betrouwbaarheidsintervallen en het opstellen van beslisfuncties, waarin telkens frequentistische en Bayesiaanse methoden worden beschreven en vergeleken. Voorts wordt ruim aandacht geschonken aan de keuze voor de prior, die afhangt van zowel het statistisch model in kwestie, als het beoogde doel van de posterior.
Dit vak gaat niet in op computationele aspecten, en slechts in beperkte mate op niet-parametrische en asymptotische eigenschappen van de posterior. Het hoofddoel is begrip van de basiselementen, waarop voortgebouwd kan worden in puur statistische vorm, of in de vorm van artificiele intelligentie en machine learning.This course is only available in Dutch
Bachelor Wiskunde
Bachelor Dubbele bachelor Wiskunde en Informatica
UvA -
Bayesian Statistics
Course - Bachelor (University)This course is part of the Mastermath programme. All information can be found at the Mastermath website.
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Bayesian Statistics
Course - Bachelor (University)At the end of this course, students can (a) name key components of Bayesian statistics (paraphrasing), (b) paraphrase basic aspects of Bayesian statistical methods, and (c) analyze psychology papers that use these methods. Based on an evaluation of the studied materials, students are also able to (d) program rudimentary probabilistic models and statistical methods (scientific thinking) that are then used to (e) analyze practical research questions (evaluation). Students can (f) report their thinking in short essays (written communication and reflection).
Bachelor’s Psychology
Exchange programme Exchange Programme Social and Behavioural Sciences
Minor Psychology Behavioural Data Science (for ISW students)
UvA -
Bayesian Statistics for Machine Learning
Course - Bachelor (University)Modern machine learning methods are based on mathematical concepts, especially from probability theory and statistics. This course treats these concepts in detail, through the spectrum of the Bayesian school of thought in machine learning. This will lay the groundwork for a solid understanding of advanced machine learning methods taught in other courses. Additionally, the mathematical theory will be made more concrete through programming exercises.
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Behavioral Decision Making
Course - Professional educationHet vermogen van organisaties om data (‘big data’) om te zetten in kennis en inzichten – ook wel business intelligence, business analytics of data science genoemd – wordt steeds belangrijker. Organisaties voorzien een toenemende behoefte aan kennis en vaardigheden die nodig zijn om data om te zetten in actiegerichte inzichten.
Om in deze behoefte te voorzien, heeft het Amsterdam Center for Business Analytics (ACBA) van de VU de postgraduate opleiding (PGO) ‘Business Analytics & Data Science (BADS)’ ontwikkeld. De PGO is gebaseerd op vakken binnen de bestaande VU-programma’s Bedrijfskunde en Business Analytics en biedt een brede, algemene basis voor iedereen die zich verder op het gebied van business analytics en data science wil ontwikkelen.
De opleiding is zowel geschikt voor personen die zich bezighouden met het omzetten van data in ‘actionable insights’ als voor personen die de resultaten van en behoefte aan dergelijke actiegerichte inzichten moeten definiëren en beoordelen (tactisch en strategisch management).
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Behavioral Operations Research
Course - Bachelor (University)The course focuses on the modeling, analysis and optimization of
complex decision making processes which involve human behavior (such as
selfish, risk-averse, altruistic or malicious behavior). Building on
game-theoretic foundations, you learn to model processes of complex
decision making and to quantify the inefficiency caused by human
behavior.The main goal of the course is to equip you with algorithmic
optimization techniques to master the challenging task of reducing the
inefficiency of such processes. These techniques find their applications
for example in Traffic Routing, Network Design, Cost Sharing,
Resource Allocation and Auction Design. -
Behavioural Data Science
Track - MasterUnderstanding data about human behaviour is an important and valuable skill in today’s society. Companies, public institutions and governmental organizations — they all use the continuous stream of big data to describe and predict human behaviour.
The police use data to predict risk of burglary by area and week of year, insurance companies adjust their prices based on client data, and schools adjust educational programmes based on what is known about student progress.
Leveraging the full potential of these massive amounts of behavioural data towards these goals greatly benefits from a thorough understanding of data science techniques and human behaviour modelling. The master’s track Behavioural Data Science aims to combine these two. The programme assumes knowledge of psychology, research methodology, and applied statistics at an undergraduate level, and continues with training on advanced (big) data techniques, academic skills, as well as practical and professional skills.
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Big Data
Minor - Bachelor (HBO)Learning about datasets is at the core of this course. When looking at datasets, students will first learn to see if they are dealing with a more traditional Data Warehouse / Business Intelligence dataset, or if there are big data related issues. With the help of technical tools for traditional data issues (ETL, DWH, Cubes) or big data issues (NoSQL, Hadoop, Mahout, R) students will either build traditional data warehouse solutions or big data solutions. This course is in Dutch.
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Big Data
Course - MasterThis course aims to provide an introduction into the main challenges that big data applications pose across all layers of a processing system, from its infrastructure to its performance. The common solutions – from design to implementation – that are being used to tackle these problems will be presented. Specifically, students will be introduced to storage and processing solutions, infrastructure options, performance challenges, systems and tools for data analytics at scale. Finally, the different success metrics to be used for these solutions will be introduced. Additionally, ethical concerns, as well as interaction with traditional data producers and consumers will be discussed. Therefore, upon completing this course, the students should be able to design a big data analysis framework, reason about its infrastructure requirements, and provide a prototype implementation using modern tools and technologies.
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Big Data
Course - Bachelor (University)Overview of big questions in (geospatial) data and its practical and ethical implications for society
– Students can demonstrate a basic knowledge of the aspects of data, including an understanding of how data is collected, stored, and used and gain practical experience in all these aspects.
– Students can demonstrate an understanding of how algorithms and visualization tools are used to extract patterns and knowledge out of data and have a hands-on experience with such tools.
– Students can demonstrate an understanding of how data is used within academia, business, and
governments and civil society.
– Students can demonstrate an insight into the societal impacts, e.g. benefits vs intrusion of privacy, that come with the datafication and have the breadth of knowledge to form and articulate a position on these issues.
– Students can demonstrate an ability to describe and evaluate their own learning in relation to the above learning outcomes.This course is open only to AUC students
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Big Data Analytics
Course - MasterBig data analytics encompasses many techniques necessary for extracting insights from large amounts of data. After this course students are familiarized with various aspect of big data analytics, and will have hands-on experience with these techniques applied to a wide range applications using a wide range of software tools (R, Excel, Tableau, ggplot, MySQL, Shiny). Students will be able to handle data from various sources, slice and dice data, build predictive and analytic models, and visualize derived data insights. Student will not only be able to conceptually explain the various machine learning techniques (supervised: regression, regression trees, bagging, boosting, random forests, k-nearest neighbours, logistic regression, classification and regression trees, support vector machines, (convolutional) neural networks, discriminant analysis; unsupervised: principal components analysis, clustering, k-means), but will also be able to explain how they overlap with statistical and psychometric methods learned in bachelor course. As this is a first exposure for students into the field of big data analytics and machine learning, students are encouraged to deepen gained insight in elective specialization courses later in the year.
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Big Data and Automated Content Analysis, Part I and II
Course - MasterThe seminar will provide insight in the basic concepts, challenges and opportunities associated with data so large that traditional research methods (like manual coding) cannot be applied anymore and traditional inferential statistics start to lose their meaning. Participants are introduced to strategies and techniques for capturing and analyzing digital data in communication contexts, through concrete examples and templates than can be shared and modified for the students’ own research projects. We will focus on (a) data harvesting, storage, and pre-processing and (b) computer-aided content analysis, including natural language processing (NLP) and computational social science approaches.
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Big Data Engineering
Track - MasterIn the internet era, data plays center stage. We all continuously communicate via social networks, we expect all information to be accessible online continuously, and the world economies thrive on data processing services where revenue is created by generating insights from raw data. These developments are enabled by a global data processing infrastructure, connecting the whole range from small company computer clusters to data centers run by the world-leading IT giants. In the Big Data Engineering track you study the technology from which these infrastructures are built, allowing you to design and operate solutions for processing, analyzing and managing large quantities of data. This track is part of the joint Master in Computer Science, in which renowned researchers from both VU and UvA contribute their varied expertise in one of the strongest Computer Science programmes available in Europe.
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Big Data for Managers
Programme - Professional educationTwo-day overview course on Big Data for (project) managers and consultants. Look beyond the hype: the terms, techniques and technologies of Big Data by experienced data-consultants and university professors.
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Big Data in Biomedical Sciences
Course - Bachelor (University)This elective addresses important concepts in bio- and neuroinformatics
and big data analysis, with powerful applications in neurosciences. Lectures
and practical assignments provide theory and hands-on experience in fast
moving fields of exploratory, graph and predictive data analytics,
neuroscience, connectomics and metagenomics. -
Big Data in Psychology
Course - Bachelor (University)Due to the growing digitisation in society, increased
international collaboration, data sharing and technological advances,
there is a vast increase in the volume and complexity of data sets that
are analysed in modern science. One social media experiment, brain
imaging study or DNA sequencing project easily encompasses terabytes of
information.
Large data volumes, commonly referred to as “Big data”, have many
advantages (more information, increased statistical power, mining for
previously unknown relations) but come with the need for special
strategies and approaches both to manage the data (transfer, storage,
updating and sharing) and process and analyse the data (cleaning,
visualization, querying, statistics, mining). In addition concepts such
as ethics and information privacy need to be considered. In this course
large data sets from experiments in cognitive and/or biological
psychology are the central theme. In the individual tutor meetings the
students run through the following steps (1); obtaining and examining
datasets and drawing-up hypotheses based on classical methods of
cognitive / biological psychology; (2) preparing the data for analysis;
(3) performing the analysis and interpreting the outcome; (5)
writing a research proposal and presenting the proposal using
PowerPoint. -
Big Data Infrastructures & Technologies
Course - Professional educationIn this module we dive into cloud technologies that allow organizations to tap into potentially thousands of computers at the click of a button at little upfront cost. We also explain the software that is used to do this and also to program such compute clusters, in order to use them for addressing Big Data problems. More info here: www.cwi.nl/~boncz/bads
Please note that this course cannot be followed separately.
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Big Data Statistics
Course - MasterNowadays it is easy to measure, collect and store a large number of
observations. However, many of the traditional inferential methods were
developed for small data sets. While the questions traditional
inferential methods tried to answer like ‘What can we learn from the
data’ or ‘How can we use the data to make predictions for future
observations’ are as important as they were many years ago, the methods
we need to answer these questions when confronted with big data sets
have been developed only recently. In this course, we will first study
the methods that have been introduced for hypothesis testing of large
data sets. Next, we learn more about regression models for big data
sets. Moreover, we discover some of the possible pitfalls arising from
electronic computation and study selective inference. -
Big Data Strategy & Implementation
Course - MBAAfter this course, the student should be able to: Drive focus on the critical Big Data opportunities (goal); Assess readiness on opportunity capture, metrics and models, technology, and people (situation); Develop a coherent vision and road-map to capture (direction); Lead a Big Data initiative to success (execute).
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Big Data Technologies for Data Science
Course - MasterIn this course we dive into cloud technologies that allow organizations to tap into potentially thousands of computers at the click of a button at little upfront cost. We also explain the software that is used to do this and also to program such compute clusters, in order to use them for addressing Big Data problems.
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Big Data Tools
Course - MasterDeze cursus heeft als belangrijkste doel het praktisch te leren omgaan
met kleine en grote biologische data sets. Technieken voor het werken
met bestanden en databases, het samenvoegen van tabellen, het grafisch
weergeven en analyseren van grote data sets en reproduceerbaar werken
zullen behandeld worden. De technieken worden geoefend aan de hand van
gegevens uit verschillende vakgebieden van de biologie. De gebruikte
programmeertaal is R.This course is only available in Dutch
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Big Data, Human Rights and Human Security
Course - Bachelor (University)The question addressed in this course is how digital technologies and
big data are used to make decisions in human rights and security
domains, and how these uses require us to rethink the basic tenets of
existing legal norms and practices. The course focuses on three human
rights and security domains in particular: big data and social and
criminal justice; the use of digital technologies in warfare and the
fight against terrorism; and the use of technology in border management
and migration law. Students will become familiar with the legal
framework regulating the collection, use and analysis of big data, and
the storage and exchange of personal data through the use of digital
technologies. For this purpose, they will be introduced to EU and
international laws on privacy and data protection, and these laws will
be fleshed out with regard to the three case studies.
Data privacy laws can serve as tools to focus microscopically on the
ways in which the use of big data and digital technologies pose
challenges to individual rights, and clarify possible remedies for these
challenges (for example by de-identifying data, foregrounding the
question of consent to the collection, use, or disclosure of the data,
or through the concept of purpose limitation). Students will learn how
to apply the principles of data protection laws in the three human
rights and security domains, and become aware of the particular problems
that feature in these particular domains. But through the case studies,
the course will also require students to reflect on the more fundamental
question as to what extent legal responses to human security threats are
undergoing a fundamental shift through the use of modern technologies.
Thus, it has been argued that these technologies facilitate a
“fundamental jurisprudential shift from our current ex post facto system
of penalties and punishments to ex ante preventative measures that are
increasingly being adopted across various sectors of society.” Whether
such a shift to from individual justice to so-called actuarial justice
is currently taking place will be discussed through looking at diverse
fields such as predictive policing, the use of smart city technology for
crowd control and surveillance, the use of big data and algorithmic
decision-making for tracking, capturing, killing or blacklisting
suspected terrorists, and the use of modern technologies in the
implementation of border and immigration policies.This course is only available in Dutch
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Big Data, Small Data
Course - MasterToday’s digital and information-dense society produces a massive amount
of data. Much of this data is generated by or related to human behavior
and can inform social scientists about societal dynamics. Examples
include social media data, parliamentary minutes, email collections or
collections of stories that have been made available in a digital
format. In this course, students learn about such digital trace data,
that are often voluminous, unstructured, and/or embedded in complex data
structures – we refer to such data as Big Data. Students learn about how
Big Data differs from data generated by traditional social science
research methods and the opportunities and challenges that Big Data pose
in present day society. They are introduced to R, a programming language
which they will use to gather and link data, and to make sense of these
data. Students learn about ways to analyze data derived from social
media such as forums or social network sites by using both computational
and interpretive approaches. The latter are key to Small Data, data with
meaning to individual citizens. -
Bioinformatics & Systems Biology
Minor - Bachelor (University)This minor is open to undergraduate students in Computer Science, Information, Multimedia and Management, Lifestyle Informatics, Biology, Medical science, Biomedical Sciences, Chemistry, Mathematics, Physics and students of related courses. Also students in 3rd or 4th year of a Bachelor Bioinformatics study are welcome. With this minor, you can move on to MSc Bioinformatics (and Systems Biology).
In the first two months, this minor introduces you to Bioinformatics and Systems Biology and examples from scientific research. The last three months are used to provide supplementary knowledge, for example, programming for students with a Bachelor in Biology, Biology for students with a background in Computer Science and Mathematics or Statistics for students of HBO Bioinformatics training.
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Bioinformatics for Translational Medicine
Course - MasterObservations from biological high-throughput experiments will allow us to improve diagnosis and give a personalised treatment plan for patients. However, integrating data from several sources and using this data for predictions is non-trivial. This is a theoretical and practical Bioinformatics course on computational methods for Translational Medicine; we will focus on Bioinformatics algorithms that are used to predict the clinical outcome for patients and analysis methods to obtain deeper understanding of complex diseases, by combining data from various high-throughput experiments such as proteomics, microarrays and next-generation sequencing as well as existing biological databases.
Bioinformatics
Physics
Chemistry
Mathematics
Computer Science
Biology
Biomedical Sciences
VU -
Biosystems Data Analysis
Course - MasterIn the analysis of biochemical systems, many measurements are performed, leading to complex multivariate data sets. The tendency is to measure more and more of just a few samples. Multivariate data analysis methods are often used to explore such sets. This course covers a broad range of multivariate data analysis methods, for e.g. exploration, clustering, classification. The latter is especially important in biomarker discovery. Design of experiments and ANOVA for multivariate data is also discussed. Furthermore, the interpretation of selected features in terms of function and networks is discussed.The course starts with an introduction on the properties of the different types of functional genomics data.
Bioinformatics and Systems Biology
Computational Sciences
Chemistry
Forensic Sciences
UvA/VU -
Brain & Mind
Minor - Bachelor (University)The purpose of this minor is to acquaint the student with the different fields of Neuroscience. The student will gain insight into the latest knowledge of how the brain works and also how this knowledge can be used to understand cognitive processes, social interactions between individuals, anti-social behavior as well as different brain diseases, such as depression, addictions, attention, or eating disorders. The nature-nurture debate will be discussed as well as recent updates in human genome research. In addition, the minor provides an introduction into the fields of neuro-economics (decision making) as well as into recent scientific technological advances in brain-machine interfaces, deep brain stimulation, and robotics. The integration between disciplines, such as biology, psychology, sociology and genetics plays a central role in this minor. Students learn to think critically about how knowledge of the brain and the human genome can be applied to deal with societal issues.
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Business Analytics
Programme - Bachelor (University)Business Analytics at VU Amsterdam is a unique program by bringing together a combination of disciplines to prepare you for business success. You will learn how to collect and manipulate large data sets, how to analyse these data using statistics and data mining techniques, and how to use your findings to predict the future and make optimal decisions. You will work on a variety of business problems that modern businesses face every day. An example is the prediction of workload, cargo and the number of passengers travelling via Schiphol or specific airlines. Another example is product pricing for big fast food chains, or segmentation and profiling of customers. You may also work on detecting fraud based on a large dataset of financial transactions, or finding ways to mitigate financial risks for banks or insurance companies. It’s all about turning (big) data into smart decisions.
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Business Analytics
Programme - MasterThe Master’s in Business Analytics (BA) is a multidisciplinary programme, aimed at improving business processes by applying a combination of methods based on mathematics, computer science and business management. You will be trained in recognizing and solving in-company problems. BA is a hands-on programme: you will use your expertise in the various fields to improve business practices by examining and analysing real-world situations as faced by companies daily.
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Business Analytics & Data Science (PGO BADS)
Programme - Professional educationThe postgraduate programme Business Analytics & Data Science (PGO BADS) is based on courses in existing VU programmes on Business Administration and Business Analytics and offers a broad, generic basis for everyone who wants to develop her-/himself in the area of business analytics and data science.
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Business Analytics and Data Science
Minor - Bachelor (University)To prepare optimally for the Business Analytics Master’s you can opt for the minor in Business Analytics. In this minor you will be offered courses that provide additional background in the fields of mathematics, computer science and logistics.
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Business Data Science
Programme - PhDThe Research Master Business Data Science is a multidisciplinary research program in which course instruction is provided by top scholars from the three participating Schools with a central focus on the performance of academic research within business disciplines, such as entrepreneurship and innovation, finance, human resources and organization, marketing, and supply chains analytics. The Research Masters prepares talented and motivated students to start their doctorate at one of the Schools in business and economics of the three partner universities or any other high quality PhD program in Business. A job market support program for PhD students will support BDS graduates to enter the international academic job market.
Data Science Foundation – Acquiring skills. In year 1, the primary objective is to build a solid data science foundation and expose students to a variety of methodological approaches. These skills are applied to various business disciplines in the field courses. Business Foundation – Building knowledge. In year 2, students focus on a given business subdiscipline, selecting from among: 1) quantitative finance, 2) management science, and 3) supply chain analytics. The courses assigned for each of these sub-disciplines have been carefully selected by a team of experts with the aim of ensuring the perfect learning trajectory that will lead to substantive contributions in the fields of each particular sub-discipline. Research Practice – Aligning skills and knowledge. The program starts with an overview of the business problems that data science can address (in block 0), which also exposes students to fundamental components of the different business fields. This early exposure helps students to absorb and process materials presented later in courses on methodology, with respect to the various business perspectives. Students become further acquainted with the different business fields during seminars held throughout the first year, for which they will have to write a research proposal, as well as during the research hackathon. The research hackathon makes students think about how to approach the problems that arise in the various disciplines, and puts their knowledge to the test. Finally, the research clinic and the Research Master thesis represent students’ final moments of integrating business and data science, and will showcase their ability to identify relevant problems and address them using cutting-edge techniques to make a substantive contribution to the field.
UvA, VU & Erasmus -
Business Intelligence & Analytics
Course - Bachelor (University)Data is hot! How organizations deal with the overabundance of data and the ability to transform data into insights have become critical success factors for every organization. Key words in this context are ‘big data’, ‘data science’, and ‘data-driven decision making and innovation’. This course offers the handles that are needed to fully deploy the potential of data, and business intelligence & analytics solutions in order to create competitive advantage. The course primarily has a managerial focus, technology will be used primarily to create hands on experience with relevant BI&A technologies and as such enhance insights in their features and characteristics. There is a lot of business involvement in this course: experts from industry and BI&A consultants will share their insights and experience in the weekly workshops.
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Business Intelligence & Analytics
Course - Professional educationThis module focuses on: objectives and design of the Business Intelligence & Analytics (BI & A) function in organizations, design and content of important BI & A processes, and BI & A project management.
Please note that this course cannot be followed separately.
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Business IT & Management
Programme - Bachelor (HBO)Business IT & Management is one of the learning paths of the HBO-ICT bachelor programme. In this programme you will gain the knowledge and experience needed to bring ICT and company strategy together. You are a networker and are able to easily communicate with stakeholders on different levels: managers, users and programmers. You combine these social skills with your knowledge of ICT, allowing your projects to run smoothly and in time. The resulting ICT applications will help companies to work more efficiently and more customer-friendly. Business IT & Management is one of the learning paths within the HBO-ICT bachelor programme. Other learning paths are Game Development (GD), Software Engineering (SE), System and Network Engineering (SNE) and Technical Informatics (TI). This course is in Dutch.
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Business Modelling and Requirements Engineering
Course - Bachelor (University)Requirements engineering (RE) is concerned with the identification of the goals that need to be achieved by an envisioned system, the operationalisation of these goals into services and constraints, and the assignment of responsibilities for the resulting requirements to agents such as humans, devices, and software. The processes involved in RE include domain analysis, elicitation, specification, assessment, negotiation, documentation, and evolution. Getting high-quality requirements is difficult and critical. Recent surveys have confirmed
the growing recognition of RE as an area of utmost importance in software engineering research and practice. Within this course, we put an emphasis on requirements elicitation,
specification, and modelling. The overall goal is to teach the key concepts related to RE and to create an awareness for the importance of this topic in practice. The various lectures and instructions will be devoted to the following topics:
• Introduction to Requirements Engineering: In this lecture, you will learn what RE is all about, its aim and scope, its critical role in
system and software engineering, and its relationship to other disciplines. You will also learn what requirements are, what they are not, and what good requirements are.
• Requirements Elicitation and Evaluation: In this lecture (and the corresponding exercise session), you will learn a variety of techniques that we may use for understanding the domain in which a software project takes place and for eliciting the right requirements for a new system. What is more, you will learn how to evaluate the elicited requirements.
• Requirements Specification and Documentation: In this lecture (and the corresponding exercise session), you will learn how to specify requirements in a proper way. Among others, we will discuss templates in natural language, diagrammatic notations, and formal specification methods for critical aspects of the to-be system.
• Modelling System Objectives: In this lecture (and the corresponding exercise session), you will learn how to model the functional and non-functional goals of the to-be system. The models you will learn to create are referred to as goal models.
• Modelling Conceptual Objects: In this lecture (and the corresponding exercise session), you will learn how to capture the structural perspective of the system. More specifically, you will learn how to use UML class diagrams to characterise, structure, and inter-relate the conceptual objects manipulated in the system.
• Modelling System Operations and Behaviours: In this lecture (and the corresponding exercise session), you will learn how to model the functional and the behavioural perspectives of the system. To this end, you will learn how to create operational models and behavioural models. -
Business Process Optimization
Track - MasterYou will tackle quantitative business problems with the aid of mathematical algorithms which are then implemented in decision support systems.
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Business Simulation
Course - Bachelor (University)A practical introduction to the different aspects of simulation. During this course we study the different aspects of Monte Carlo simulation and discrete-event simulation in a coherent way. Subjects treated are: Modeling of business problems, statistical outcome analysis, simulation optimisation, software tooling, programming of simulations in Java.
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Business Statistics
Course - MasterInternational business administration is a subject in which data are of
prime interest. Is there convincing evidence that your online marketing
campaign results in more sales than your standard newspaper adds? Or
that your increase in employees’ schooling budgets increase company
loyalty? As a business professional, you want to act on evidence, not
only on your gut feeling. Statistics is the key tool that helps you to
analyze and make sense of empirical evidence and to support informed,
data-driven decision making. These skills are highly valued in todays
labor market.This course is part of your methodological toolkit and the
methodological learning line of your program. It builds on your aptitude
with symbols as built in Business Mathematics, and your critical
academic evaluation as trained in Academic Skills. The course also leads
up to a further deepening of tools in Business Research Methods, and
applications as dealt with in your courses on Business Processes,
Integrative Project, and the Bachelor Thesis. -
Calculus 1
Course - Bachelor (University)At the end of this course students are familiar with some basic principles of functions of one real variable, like limit, continuity, derivative and (improper) integral. They also know some important theorems about these topics and are able to solve exercises with various calculus techniques.
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Calculus 2
Course - Bachelor (University)At the end of this course students are familiar with some basic principles of series, of functions of several real variables and of ordinary differential equations. They also know some important theorems about these topics and are able to solve exercises with various calculus techniques.
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Classical Internet Applications
Course - MasterPlease refer to the System and Network Engineering web pages for detailed and current course information.
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Coding the Humanities
Course - Bachelor (University)Coding skills are increasingly in demand: they enable scholars to develop the appropriate applications for processing and analyzing data, either big or small.
This course teaches foundational coding skills using Python (a popular programming language), with the goal of:
- helping students and researchers to understand when and how to automate a task or analyze data programmatically;
- developing custom applications, rather than using ready-made ones, which can benefit the actual practice of humanities research as well as its outputs.
The course introduces foundational programming concepts (variables, data types, flow control, functions, input/output), and mentions useful extensions focused on data analysis (Pandas) and natural language processing (Natural Language ToolKit).
This is the first course in coding, therefore no prior knowledge is required.
Media Studies Elective
Bachelor in Media and Information
Bridging Programme in Cultural Information Science (Media and Information)
Amsterdam Exchange programme – Humanities
Minor Digital Humanities
College of Humanities Elective
UvA -
Combinatorial Optimization
Course - Bachelor (University)In this course you will learn about the theory of combinatorial optimization problems. Also, you will apply the theory to model and solve complex problems using the available software.
In particular, we consider performance measures for algorithms for combinatorial problems such as the running time and the quality of solutions. -
Communication & Multimedia Design
Programme - Bachelor (HBO)In the study Communication and Multimedia Design (CMD) you will learn to design digital media with a view to the end user. This enables products, services and even entire organizations to improve. This programmes is in Dutch.
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Computational Complexity
Course - MasterComplexity theory deals with the fundamental question of how many resources, such as time, memory, communication, randomness, etc., are needed to perform a computational task. A fundamental open problem in the area is the well-known P versus NP problem, one of the Clay Millennium problems. In this course we will treat the basics of complexity theory, NP-completeness, diagonalisation, Boolean circuits, randomised computation, interactive proofs, cryptography, quantum computing, and circuit lower bounds.
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Computational Intelligence
Track - MasterIn the course Computational Intelligence, we will focus mainly on computational aspects of Artificial Intelligence, namely, optimization algorithms for solving learning problems. Specifically, we will consider problems that cannot be solved using information about gradient due to their combinatorial character or complexity of the objective function (e.g., non-differentiability, blackbox objective function). These problems pop up in computer science and AI, such as, identification of biological systems, task scheduling on chips, robotics, finding optimal architecture of neural networks. For this purpose, We will introduce different classes of algorithms that can be used to tackle these problems, namely, hill climbing and local search, and evolutionary algorithms. Additionally, we explain sampling methods (Markov Chain Monte Carlo) and population-based sampling methods, and indicate how they are linked to evolutionary algorithms. In the second part of the course, we will discuss neural networks as current state-of-the-art modeling paradigm. We will present basic components of deep learning, such as, different layers (e.g., linear layers, convolutional layers, pooling layers, recurrent layers), non-linear activation functions (e.g., sigmoid, ReLU), and how to use them for specific problems. At the end of the course, we will touch upon alternative approach to learning using Reinforcement Learning and Bayesian Optimization. We will conclude the course with a recently revived field of neuroevolution that aims at utilizing evolutionary algorithms in training neural networks.
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Computational Methods in Econometrics
Course - Bachelor (University)In this course we discuss numerical and simulation-based methods for their use in econometrics and data science. In the first part we review matrix computations, numerical methods for optimization and Monte Carlo integration. We illustrate how these methods are used for estimation of parameters in statistical models. In the second part we investigate properties of estimators, test statistics and residuals using simulation studies. In particular, we simulate distributions of parameter estimates under different data generation processes, but also distributions of
test statistics such as unit-root tests, of R-squared goodness-of fit in spurious regressions, and of model selection criteria such as Akaike information criteria. We finally use simulations to verify the accuracy of diagnostic tests related to normality and heteroskedasticity. -
Computational Science
Programme - MasterUnderstanding and predicting developments in our complex world, and transforming them into advanced computer models is what the Master’s programme in Computational Science is all about. This is a joint UvA and VU degree programme. You will look at important questions facing the world now, and the ones it will face in the future, whether they relate to forecasting financial markets, anticipating human behaviour in crisis situations or studying future cities. You will have the opportunity to focus on your specific field of interest, varying from biology and chemistry to mathematics and finance.
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Computer Science
Programme - MasterComputer Science studies the technology that has become ubiquitous in our global, connected society. Traditionally, the computer had been the primary object of study. Nowadays, globally distributed information processing services have taken center stage, with the Internet connecting a wide variety of information processing devices, ranging from mobile phones to data centers operated by the world leadership companies. Computer Science at the VU and UvA is very broad and wide-ranging compared to other universities. You will choose a specialization/track as part of the programme. Besides the compulsory courses, you will have the opportunity to take optional courses from the whole range of computer science.
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Computer Science (Informatica)
Programme - Bachelor (University)Computer Science is all about our interaction with information. That information can range from railway timetables to personal health care data or exciting new virtual games. Computer technology has given us a wealth of new opportunities, but unfortunately it has also created serious risks. The Bachelor’s in Computer Science at VU Amsterdam focuses on both aspects, with Networks and Security as two major topics in the program. The openness and transparency that we value in the Netherlands are reflected in our educational system. You will receive a pro-active and challenging education, and our lecturers’ doors are always open for any questions you might have. Our lecturers combine teaching with top-level research. They also maintain solid relations with various businesses in the field, and projects you will work on during your program are often organized in cooperation with such companies.
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Computer Science (Informatica)
Programme - Bachelor (University)The Bachelor’s in Computer Science is offered by the UvA. You will learn to understand computer technology, in all its complexity and about possible applications for the future. During the first year, you will get to know more about Computer Science in the broadest sense, covering subjects such as computer architecture and operating systems. In the second and particularly in third year, you will have the opportunity to focus more on your specific field of interest, which can vary from software engineering to graphics and game technology.
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Computer Science (Technische Informatica)
Programme - Bachelor (HBO)Hardware and software are inseparable: robots in industrial production lines to operating systems of tablets and smartphones. This Computer Science programme is part of the training HBO ICT. You will learn to program for both hardware and software, and specifically hardware and software programs for Embedded Systems, Industrial Automation and Robotics. For this program, it is important that you are willing to learn and understand how technology works. Furthermore, you must have mathematical insight, an inquisitive attitude and enjoy working in a team. At the end of this program, you will be able to develop and maintain innovative ICT solutions, including computer networks, processors, robotics and embedded systems. HBO ICT consists of the learning routes: Business IT & Management (BIM), Game Development (GD), Software Engineering (SE), System and Network Engineering (SNE) and Computer Science (CS).
This course is only available in Dutch.
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Computer Science: Computer Systems Security
Course - MasterThe Master track in Computer Systems Security focuses on system and security issues related to operating systems, hardware and applications (topics like hacking, malware, reverse engineering, vulnerabilities). This specialization in Computer Systems Security is a joint effort by VU Amsterdam and University of Amsterdam. The emphasis on system related issues is what sets this track apart from other master programmes on security, which tend to have a focus on formal methods or the math behind cryptography. You’ll be taught by leading researchers in the field of computer security. Many of the challenging courses have a very hands-on character.
Students graduating in the Computer Systems Security specialization have knowledge of
- security issues in system-level software including weaknesses and defenses;
- static and dynamic analysis techniques for software (benign and malicious);
- modern scalable computer and network architecture;
- secure software development for modern, highly parallel computer systems.
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Computer Systems Security
Track - MasterThe Amsterdam-based Master track in Computer Systems Security is unique in the Netherlands in focusing on system security issues in operating systems, hardware and applications. Have you ever wondered how attackers bypass even the most advanced security mechanisms, how we can reverse engineer state-of-the-art malware, or in general, what “hacking a system” is all about? This specialization in Computer Systems Security, a joint effort by VU University and University of Amsterdam, is different from security tracks in other universities which tend to focus more on formal methods or the math behind cryptography. Instead, we focus on systems. Our philosophy is that you learn by doing and, moreover, the way to learn most about security solutions is to break them. So, you will learn how to write exploits and how to bypass some of the strongest defenses commonly deployed. The courses are extremely challenging and most of them have a very hands-on character.
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Computer Vision 1
Course - MasterDigital cameras have become ubiquitous in the form of consumer cameras, webcams, mobile phones, and professional cameras. These cameras yield enormous streams of data and provide the means for communication, observation, and interaction. In this course, image understanding is addressed with the focus on core vision tasks of scene understanding and object recognition.
A broad range of techniques are studied on how computers can understand the visual world of humans including image formation and filtering, features (color and shape invariants, interest point detectors, descriptors, SIFT, HoG), visual information representation (vector space, statistical models, bag-of-words), learning and classification (nearest neighbor, kernel density estimation, SVM), dimension reduction (PCA, LDA and SVD), object detection and classification, object tracking (mean-shift, Kalman), and user interaction (active learning).
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Computer Vision 2
Course - MasterThe field of computer vision is by impact one of the forefront fields of AI.
Computer vision has matured to a degree that many applications have become possible or nearly are possible.
In this course, computer vision is seen as an enterprise that uses statistical methods to disentangle image data using models constructed from geometry, physics, and machine learning.
The course aspires a thorough understanding of many of the current techniques, aiming at a broad basis, and extending to state of the art methods. Topics range from texture analysis, (photometric) stereo, and multiple view geometry, to sophisticated techniques of model based vision, material recognition, tracking, object detection, and scene classification.
To appreciate the nowadays possibilities, practical experience will be gained with some state-of-the-art techniques.
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Concurrency & Multithreading
Course - Bachelor (University)This course provides a comprehensive presentation of the foundations and programming principles for multicore computing devices. Specific learning objectives are: To provide insight into fundamental notions of multicore computing and their relation to practice: locks, read-modify-write operations, mutual exclusion, consensus, construction of atomic multi-reader multi-writer registers, lost wakeups, ABA problem. To provide insight into algorithms and frameworks for multicore computing and their application in multi-threaded programs: mutual exclusion algorithms, spin locks, monitors, barriers, AtomicStampedReference class in Java, thread pools in Java, transactional memory. Analyzing algorithms for multicore computing with regard to functionality and performance: linearizability, starvation- and wait-freeness, Amdahl’s law, compute efficiency gain of parallelism. Mastering elementary datastructures in the context of multicore computing: lists, queues, stacks. Programming in multi-threaded Java, and performing experiments with such programs.
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Consumer Behaviour
Course - Professional educationA number of disruptive innovations are coming to the consumer space and more generally to every consumer’s life. Companies, governments, and technology influencers like Stephen Hawking and Elon Musk emphasize the need for understanding how consumers/employees will react, and advocate a focus on the behavioural and ethical consequences.
This course takes a future-oriented perspective above and beyond current developments such as social media and “big data”, and aims to enhance the understanding consumer behaviour in the age of disruptive innovations. In detail, it
1) introduces a number of (marketing-relevant) disruptive innovations, such as:
- Humanoids and Robots
- Human Enhancement Technology & Cyborgism
- Artificial Intelligence
- Blockchain
- Virtual Reality and Augmented Reality
- Future of Food (sonic-enhanced food, lab-grown meat, insects)
2) teaches theories from marketing, psychology, neuro-science and information science to explain consumer acceptance, biases, and behavioural change upon exposure to these disruptive innovations,
3) provides insights into (theory-based) strategies companies can apply when introducing and using disruptive innovations with a focus on consumers (e.g., in product design, launch communication).
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Corporate Finance
Course - Professional educationThe course builds on the foundations provided by the courses Finance / Financiering, Investment and Portfolio Theory 1 and Investment and Portfolio Theory 2. Starting from the unifying principles of value maximization and the law of one price, we discuss how successful organisations value and identify profitable investment opportunities, manage their capital structures and create shareholder value. Some of the topics covered during the course include: capital budgeting and valuation methodology, managing and valuing real options, the choice between debt and equity, managerial incentives and executive compensation, mergers and acquisitions, restructuring, initial public and seasoned equity offerings and time permitting, corporate governance. Our discussion of the key concepts of corporate finance will be illustrated with real-world business cases and analysed with rigorous methodology.
The aim of this course is to provide an in-depth understanding of the tools and concepts of modern corporate finance. After successfully completing this course students:
- understand the economic goals corporate finance policies are meant to achieve;
- have learned how to learn and develop the skills to propose and defend ideas;
- effectively and enthusiastically participate in real-world competitive and professional environments.
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Corporate Strategy and Organisation Design
Course - Professional educationThe scope of corporate strategy goes beyond the individual product market, instead focusing on how firms can create value across different businesses. Thus, how can the company create value above and beyond the value created by the individual business units. In this course we will focus on three main decisions: Corporate governance (ownership vs. control and stakeholder strategy), Business portfolio choices (growth and vertical and horizontal integration), and organisation (The role of corporate headquarters, organisational structures).
Upon completion of this course students have the ability to:
- explain how important theories in the field of strategy (e.g. agency theory, transaction cost economics and resource-based view) inform our understanding of firm’s corporate strategy (specifically: corporate governance, vertical (dis)integration, growth, diversification, and organisational structures).
- compare and criticise these theories, including their ethical implications for practice, based on a good understanding of their limitations;
- apply the theories discussed in the course to determine the best course of action regarding strategic issues faced by firms.
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Cyber Security
Programme - Bachelor (HBO)We hear almost everyday in the news that governments and companies have to deal with cyber attacks and data leaks. Recognized digi disruptions such as blockchain, robotisation and internet of things require specialists who can provide a stable and secure ICT service. The System and Network Engineering (SNE) learning path has therefore been changed to Cyber Security (CS). The courses in the area of CS have been tightened up in terms of content and the focus is shifting to CS. You learn to analyze, design, realize and manage technical infrastructures on the basis of a system and network architecture. For this you must be able to work accurately and be interested in control and computer systems, new technologies and the internet. You advise companies which ICT solution is best for their organization and ensures stable, secure connections, networks and systems. HBO-ICT consists of the learning routes Business IT & Management (BIM), Game Development (GD), Software Engineering (SE), Cyber Security (CS) and Technical Informatics (TI). This course is in Dutch.
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Cybercrime and Forensics
Course - MasterDigital Forensics on Systems and Networks are the main focus-point of the course. Topics are a proper forensics methodology to acquire and analyze evidence from a wide variety of sources. Legal as well as ethical aspects of digital forensics are discussed. In a forensics project students work to develop new techniques and prototype tools to further the field.
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Data Analytics & Quantitative Trading
Course - MasterThis course teaches how to apply computing technology to financial trading strategies. This is a broader set of skills than simply writing programs that execute trades. Students who complete this course will gain intuition into how financial trading strategies depend on relationships between different securities’ prices, or between prices and firm characteristics. Students will also learn that an algorithm’s fundamental purpose is to provide a set of detailed instructions for a computer to execute.
To achieve these goals, the course will demonstrate how to use the Python language to program various financial algorithms, such arbitraging price discrepancies between related securities or factor models that identify stocks with high returns. The first 1/3 of each lecture will explain the theoretical relationships behind each financial trading strategy. The other 2/3 of each lecture will demonstrate how to write programs that execute the strategies. Throughout the course, students will also learn the limitations of trading algorithms.
The programming section of each lecture will emphasize the importance of breaking down a trading strategy into a specific set of steps. It will then show how to solve each step using Python. Weekly in-class assignments will help students to gain practice with coding.
By the end of this course, students should possess a good understanding of the foundation of financial trading algorithms, and how to think like programmers. The goal is to provide enough knowledge of the fundamentals of finance and computer science that students can subsequently teach themselves how to program additional trading strategies.
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Data Journalism
Course - MasterOne of the most important recent innovations in journalism is the increasing use of data. Often referred to as data journalism, we see a development of using computational techniques to make use of, for instance, massive sets of documents (e.g., leaks), or government data, provided via APIs or scraped from the web.
In short, the increased availability of digital data, fueled by the trend towards open governance or the use of online media, has opened new ways for journalists to discover and research interesting and relevant stories. While the use of data in journalism is not new, the amount of data and their digital nature require new skills from journalists. At the same time, audiences are demanding greater transparency from news organizations, and the news cycle is ever-more choked with content, both of which challenge journalists to use data in ways that are creative, compelling, transparent, and innovative.
This course combines discussion of these developments with practical skills training. Students will be introduced to the programming language Python, widely used for retrieving data from the web and analyzing both textual and numerical data. Additional topics include data visualization, finding stories in large amounts of data, and cleaning messy data.
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Data Mining Techniques
Course - MasterThis course surveys basic data-mining techniques and their application in solving real-life problems in such areas as marketing, fraud detection, text and web mining, and bioinformatics.
Business Analytics
Computational Science
Artificial Intelligence
VU/UvA -
Data Mining Techniques
Course - Bachelor (University)The course is intended to introduce Data Mining Techniques to students that are new to the field as well as to more experienced students. The main aim is to gain a more practical perspective towards Data Mining Techniques/Machine Learning. Lectures will cover more basic things for those new to the field (general introduction into Data Mining, classical algorithms such as decision trees, association rules, neural networks, ensemble learning, etc.) and on top will discuss advanced topics including deep learning, recommender systems, big data infrastructures, and text mining. A number of successful applications in the area will also be discussed. In addition to lectures, there will be an extensive practical part, where students will experiment with various data mining algorithms and data sets. The grade for the course will be based on these practical assignments (i.e., there will be no final examination).
Computer Science (Joint Degree)
Artifical Intelligence
UvA /VU -
Data Processing
Minor - Bachelor (University)In this course you will build your own toolkit of useful programs for processing and visualizing data that you may encounter in your field. The theory of visualizations will be discussed. After this course you can follow the programming project to apply your knowledge to a project examples in your own field.
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Data Science
Course - Bachelor (University)In this course you will work with large databases, often the web. Both text and numerical data will be used. Analyses will mostly be with the Python’s module pandas. Specifically, you will learn to edit typical data science data: (very) large amounts of text with annotations, represented in a database, spreadsheet or XML, or as a collection of text files, for scientific research. This includes transforming, annotate, categorize, classify and organize data. All through computers, and as little as possible by hand. You will learn to evaluate the quality of a computer program by edited data. You will learn to work quickly and effectively with ipython notebooks, Python, and various modules for Data Science, in particular, pandas. You will gain knowledge of results with eHumanities and computational social science techniques, and the code to reproduce similar results in comparable datasets.
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Data Science
Track - MasterIn the one-year Data Science Master’s track, you will acquire knowledge of the theories and tools used in data science. We will teach you how to use these tools for working with data in different domains, such as Healthcare, Media and Communication, Smart City, Life Sciences and Digital Humanities. Graduates have an integrated view on the possibilities and development of data science in society.
Students will benefit from the strong collaboration with Amsterdam Data Science (ADS), bringing together leading researchers across the entire life cycle of data science, from expertise in machine learning and information retrieval to human computer interaction and large-scale data management.
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Data Science and Business Analytics
Track - MasterThe Econometrics Master is a multi-disciplinary programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages such as Eviews, R and Matlab, to explore and analyse problems in economics and finance.
The specialisation/track Data Science and Business Analytics deals with large and complex data from widely different sources for the use in economics and business.
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Data Science Essentials – online modules
Programme - Professional educationThis interactive programme offered by the Amsterdam Business School will be taught by experienced university lecturers and consultants, who will share their knowledge and expertise on the topic. There will be plenty of room for discussion and work-related questions.
Modules
Join 1 or all of the 5 hands-on Data Science Essentials modules, the next edition will start in 2021. You will learn the key concepts of working with data and analytics techniques and grow the skills to efficiently run data science projects, answering your organisation’s key questions.
Module 1: Python Programming Skills
Module 2: Data Cleansing and Visualisation
Module 3: Business and Soft Skills
Module 4: Applied Machine Learning
Module 5: Applied Optimisation -
Data Science for Auditors 1
Course - Professional educationDe module DSA1 heeft als doelstelling om de student de eerste beginselen van data science in auditing bij te brengen. Doel is het zelfstandig kunnen ontwikkelen van onderdelen van een datagedreven audit-aanpak, waarbij eenvoudige analysetechnieken in de praktijk kunnen worden gebracht. Na afloop moet de student in staat zijn zelf ideeën op dit gebied te ontwikkelen, de opgedane kennis in de praktijk toe te passen, om te gaan met onvolledige informatie over dit onderwerp en zijn of haar kennis met andere professionals te delen.
De stof wordt behandeld aan de hand van een casus, waarbij de data-gedreven accountantscontrole van een havenbedrijf centraal staat. Binnen deze casus lost de student eenvoudige vraagstukken op in de programmeertaal R in een speciaal hiervoor ontwikkelde Jupyter-omgeving.
De module sluit aan bij de Eindtermen met raakvlak stream ICT – variant Assurance uit de Eindtermen Accountantsopleidingen, voor zover deze niet in andere modules aan de orde komen.
This course is only available in Dutch
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Data Science for Auditors 2
Course - Professional educationDe module DSA2 bouwt voort op DSA1 en heeft als doelstelling om de student bij te brengen hoe uit de statistiek bekende exploratieve en confirmatieve analysetechnieken in een datagedreven audit-aanpak kunnen worden ingezet. Na afloop moet de student in staat zijn zelf ideeën op dit gebied te ontwikkelen, de opgedane kennis in de praktijk toe te passen, om te gaan met onvolledige informatie over dit onderwerp en zijn of haar kennis met andere professionals te delen.
De stof wordt behandeld aan de hand van een aantal cases, waarbij de data-gedreven accountantscontrole centraal staat. Binnen deze cases lost de student eenvoudige vraagstukken op in de programmeertalen R en Python in een speciaal hiervoor ontwikkelde Jupyter-omgeving.
De module sluit aan bij de Eindtermen met raakvlak stream ICT – variant Assurance uit de Eindtermen Accountantsopleidingen, voor zover deze niet in andere modules aan de orde komen.
This module is only available in Dutch
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Data Science for Auditors Principles
Course - MasterNa het afronden van deze module beschikt de student over kennis, inzicht en vaardigheden op de volgende gebieden:
- Data-driven auditaanpak
- Steekproeven en het testen van hypothesen
- Extract, Transform, Load
- Correlatie en regressie
- Classificatie
- Visualisatie
- Data-analyse in fraudeonderzoek
- Process mining
- Advanced analytics (big data, machine learning)
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Data Science Methods
Course - MasterThis course covers the basic theory of multivariate data analysis with a focus on the most relevant multivariate techniques, as well as their application to econometric data in computer practicals.
Topics include: Introduction to Python, NumPy and Pandas; data scraping, cleaning and wrangling; data visualisation; model evaluation; cross-validation; shrinkage methods: ridge regression + lasso; principal component analysis; discriminant analysis; nearest neighbour methods; model averaging.
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Data Stewardship
Course - MBABig data refers to data that are more voluminous, but often also more unstructured and dynamic, than traditional data. This concerns, in particular, data-collection that draws on Internet-based data sources such as social media, large digital archives, and public comments to news and products. One of the big challenges is to derive information from these messy or unrefined data. We will focus on (a) acquiring and storing data (b) data wrangling: cleaning, transforming, merging and reshaping the data and (c) computer-aided exploratory analysis using robust methods. Students are expected to be interested in learning how to write own programs where off-the-shelf software is not available. Some basic understanding of programming languages is helpful, but not necessary to enter the course.
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Data Structures and Algorithms
Course - Bachelor (University)The course is concerned with data structures and the design and analysis of algorithms. We study several subjects from the book by Cormen et al: linear data structures such as stacks, queues, linked lists, tree-like data structures such as binary trees, binary search trees, balanced binary search trees, heaps, graph-like data structures, and hash tables. Further we study several sorting algorithms. some graph algorithms. string matching, and the programming paradigms divide-and-conquer, dynamic programming, and greedy algorithms. We consider the worst-case time complexity and in some cases the correctness of algorithms.
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Data Systems Project
Course - MasterThis project stretches of the whole semester and will provide students of both tracks with the opportunity to apply their gained knowledge to solve a complex problem in a real world project. The Project is founded on two pillars:
1. Experience and understand the creative process of developing an interaction environment as part of research into complex systems, with a particular focus on stakeholder research, user-research, data identification, context mapping, interaction design from agile development to a technologic prototype, and evaluation (validation).
2. Stimulating personal & professional leadership, via activities that improve team building and project management skills, and activities that contribute to one’s intellectual development, autonomy and employability. These activities are either organized by the students themselves, or are offered in the form of workshops. The aim, of this course is also to introduce students to a rigours application of academic skills, such as research question formulation, experiment design, and evaluation.
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Data Visualisation
Course - Professional educationAfter this course, students should be able to: understand the purpose of various types of data visualization, ranging from infographics to visual analytics; apply design principles to design information dashboards; understand the applicability of various visualization techniques; use visualization tools to perform visual analysis. The course will be comprised of the following: advanced visualizations for numeric, categorical, temporal, and geographical data; advanced visualizations for tree and network structures; the role of perception and cognition in visualization; visual analytics models; multimedia analytics; telling stories with data.
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Data Wrangling
Course - Bachelor (University)Big data refers to data that are more voluminous, but often also more unstructured than traditional data. This in particular concerns data-collection that draws on Internet-based data sources such as social media, large digital archives, and public comments to news and products. One of the big challenges is to derive information from these messy data. The first step in this process is also called data wrangling, which is the main subject of this course. Once the data is parsed and cleaned, it is usually analysed in an exploratory way before more advanced statistical or machine learning techniques are applied.
Minor Amsterdam Data Science and Artificial Intelligence
UvA -
Data, Sensors and Complex Services
Course - MasterStudents will investigate the interface between sensors, data, APIs, machine intelligence and societal interventions with practical application to people and real world problems. Research led activities on the course will be centred around applying theory in projects involving building and programming prototypes of remote sensing devices and physical data driven interventions. Students will be have to evaluate and reflect on impact on society of data and ubiquitous computing systems as distributed data driven services.
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Data-Driven Business Innovation and Entrepreneurship
Course - MasterEntrepreneurship is fundamental to generate value-added from innovation and it is an increasingly important subject for students and professionals, also in the context of data science. The growing complexity of the data science sector and its accelerating dynamics urge professionals to think and act in an entrepreneurial way. Due to the informatization of society, data about nearly everything emerge every day. By using data, searching for inferences and patterns, may entrepreneurs help to better support their new ventures.
This course is especially useful for ambitious students who want to demonstrate their ability to analyze data in order to pursue a career as an entrepreneur or who want to work at an entrepreneurial firm. During the last hundred years, entrepreneurial innovation is the main generator of jobs and welfare in Modern Society, the “true source” of national competitive advantage. Many Universities, Research Institutes and Research Departments of large Enterprises have adopted policies to stimulate the relationship between entrepreneurship and innovation, in the hope of facilitating economic growth.
During the course, students will learn basic knowledge about how to successfully launch a new venture and its underlying business idea. Working in teams, students will be requested to collect and analyze data in order to identify and validate an innovative business idea – representing a starting point for a potential start-up – to present in a “business pitch” and a “short final report” at the end of the course. Students must support their proposal with data analysis.
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Databases
Course - Bachelor (University)The course objective is to obtain a good knowledge and understanding of relational database systems. This includes the ability to develop conceptual database models, as well as key concepts and skills in relational database model theory and practice.
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Databases & Data Visualisation
Course - Bachelor (University)Data and databases play a central role in any information system from transaction processing to enterprise systems and, of course, data science applications. The purpose of this course is to offer a solid understanding of the core concepts in this area as well as an opportunity to apply these concepts hands-on in structured exercises as well as in a much less-structured ‘living case’ setting. These core concepts are based on the relational data model and data modeling as well as SQL -the de facto standard database language – combined with data visualisation and the design of metrics and dashboards. The course includes a significant practical part.
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Deep Learning
Course - MasterDeep learning is primarily a study of multi-layered neural networks, spanning over a great range of model architectures. More specifically, the following content will be the studied both from theoretical (during lectures) as well as from practical (during the practicals) point of view.
Linear regression, logistic regression, perceptrons. A recap of previous simple machine learning models
Back propagation and optimization. Since neural networks are notoriously difficult to train, we will deepen our understanding on how to optimize them both theoretically and practically.
Convolutional neural networks(CNN). The driving force behind the popularity of deep learning. CNNs have their main application in image recognition, object detection, automatic text translation and speech recognition.
Recurrent neural networks(RNN). CNNs or other traditional network architectures have only feedforward operations. With RNNs we add feedback to the model, thus providing it with memory. With RNNs one can build machines that write Shakespeare, automatic caption generators, automatic music generators or even machines that dream new pictures.
Transfer learning. Normally deep learning requires big data. For some problems big data is not available. With transfer learning one can combine different sources of data to build a more powerful model.
Restricted Boltzmann Machines, autoencoders. These are two examples of generative deep models that can train from unlabeled data. Other variants of deep learning models and architectures will also be presented.
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Deep Learning for Natural Language Processing
Course - Master- Gain a good understanding of the deep learning architectures that are used for a spectrum of NLP problems
- Gains practical knowledge in developing one or two models or model variations to a address a concrete NLP problem
- Learns how to evaluate and analyze a deep learning model and its performance for specific NLP problems and based on this analysis learns how to formulate scientific questions
- Learns how to present and motivate deep learning modeling choices for specific NLP problems
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Deep Programming
Minor - Bachelor (University)The Deep Programming Minor is open to Bachelor students in Computer Science, Information, Multimedia and Management, and Lifestyle Informatics. The Minor includes courses in Concurrency and Multithreading, Systems Programming, Information Retrieval, Operating Systems, and Equational Programming.
Computer Science
Information
Multimedia and Management
Lifestyle Informatics
VU -
Descriptive and Inferential Statistics
Course - Bachelor (University)This course offers an overview of techniques to describe quantitative
data. Topics are, among others, mean, variance, correlation and
regression. Using the elaboration model students learn to interpret the
relation between two variables controlling for the effects of a third
variable. This course also offers an overview of statistical techniques
how to analyze collected
data in order to test hypotheses. Afterwards a student is able to
formulate hypotheses, to test them, to draw correct conclusions, to show
the relation between levels of significance, p-values, statistical
power, and statistical errors. During SPSS tutorials all techniques will
be applied.Bachelors Cultural Anthropology and Development
Bachelors Sociology
Bachelors Political Science
Bachelors Communication Science
VU -
Digital Analytics
Course - MasterThe course focuses on the process that communication and data science professionals should go through to (1) identify communication & business challenges that could be answered by digital analytics, (2) gather and understand the data available, (3) prepare the data for analysis, (4) create models, and (5) evaluate the effectiveness of the models in addressing the challenges. Central to this course are digital analytics, including web analytics, search analytics, social media analytics and journey analytics. We discuss 1) designs and procedures for gathering analytics data, 2) validity and biases of analytics data, 2) how to analyze these data, 3) how organizations and brands can use the results and optimize communications with their stakeholders, 4) privacy, security and ethical aspects. The course is not overly technical, but rather aims at advancing the student’s knowledge and understanding of digital analytics as a way to enable her or him to be part of teams that use digital data in creative ways to solve communication challenges, and to work effectively with data science or computer science professionals.
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Digital Humanities and Social Analytics
Minor - Bachelor (University)The sources and objects studied in history, media, literature, art, and social sciences are increasingly becoming available in digital formats. The minor Digital Humanities and Social Analytics will train you in how to create and analyse different types of data collections, using tools for text mining, data analysis and visualization.
The courses include hands-on training, research internships in ongoing research projects, as well as theoretical reflection on the promises of ‘the digital’ for your own discipline. Practical computational training will sharpen your analytical skills and enhance your job opportunities in the future.
To organize this minor VU Amsterdam works closely together with the KNAW Humanities Cluster in Amsterdam, where students participate in cutting edge digital humanities projects.
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Digital Humanities and Social Analytics in Practice
Course - Bachelor (University)The goal of the course is to get acquainted with digital humanities research, by collaborating in a current project through an intensive internship of one month. Students learn to put digital theory into practice, applying the knowledge gained from previous minor courses to a real-world project.
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Digital Innovation & Transformation
Course - Professional educationDo you feel a bit lost in the digital wonderland? Join the executive program to gain deep knowledge and a broad perspective on what digital transformation may mean for your organization.
Digital transformation is the radical organizational change that is due to the emergence of digital innovations such as artificial intelligence, robotics, digital platforms, innovation ecosystems, blockchain, virtual reality, and internet of things. We hear these buzzwords all around: but what do they really mean for your organization?
The program will help you navigate the digital landscape.
The executive program will help you recognize the relevant opportunities and threats to create value with digital innovation for your organization. Why? Because the program looks at the entire ecosystem: you will study the emergence, development, implementation, and prolonged usage of technologies in practice by engaging in in-depth studies at a wide variety of international organizations.
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Distributed Systems
Course - MasterIt is difficult to imagine a standalone modern computer system: every such system is one way or the other connected through a communication network with other computer systems. A collection of networked computer systems is generally referred to as a distributed (computer) system. As with any computer system, we expect a distributed system to simply work, and often even behave as if it were a single computer system. In other words, we would generally like to see all the issues related to the fact that data, processes, and control are actually distributed across a network hidden behind well-defined and properly implemented interfaces. Unfortunately, life is not that easy. As it turns out, distributed systems time and again exhibit emergent behavior that is difficult to understand by simply looking at individual components. In fact, many aspects of a distributed system cannot even be confined to a few components, as is easily seen by just considering security. In this course, we pay attention to the principles from which modern distributed systems are built. Unfortunately, these principles cannot be viewed independently from each other: each one is equally important for understanding why a distributed system behaves the way it does.
System and Network Engineering
Computational Science
Business Analytics
VU -
Dynamic Programming and Reinforcement Learning
Course - MasterThis course is concerned with reinforcement learning and its origin
dynamic programming. These are fields dealing with goal-directed
decision making over time, such as finding your way in an unknown area,
playing a game or pricing airline tickets.
We look at these areas from different angles:
– we deal with full-information “planning” problems, but also with
partial-information “learning” problems
– we consider different algorithms, some of which are guaranteed to find
the best solution, but also heuristics
– we consider high-dimensional problems (such as games) and methods to
solve them
– we look at small toy problems to understand algorithms and sharpen our
intuition, but also bigger problems for which we learn how to implement
algorithms (in python or R)
– we look at different types of applications, both from AI (search
problems, games) and OR -
Dynamics and Computation
Course - Bachelor (University)This course will give you an overview of the theory of discrete and continuous dynamical systems (first period), and a foundation in the most commonly applied numerical algorithms used to solve algebraic and dynamic problems (second period) found in concrete applications.
At the end of the course, the student is able to: analyse one and two-dimensional difference and differential equations systems; solve systems of non-linear ODEs numerically; linearise a non-linear system, compute corresponding eigenvalues (by hand and numerically), and draw conclusions on the stability of fixed points; use several numerical algorithms in concrete applications; make programs in Matlab.
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Econometrics
Track - MasterThe Master of Science programme in Econometrics is a multi-disciplinary Master’s programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages such as Eviews, R and Matlab, to explore and analyse problems in economics and finance.
The specialisation/track Econometrics emphasises statistical techniques for micro- and macro- econometrics analysis.
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Econometrics
Programme - MasterThe Master of Science programme in Econometrics is a multi-disciplinary Master’s programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages such as Eviews, R and Matlab, to explore and analyse problems in economics and finance.
Econometrics emphasises statistical techniques for micro and macro econometric analysis, whereas Financial econometrics focusses on mathematical and statistical techniques and their application to financial models and time series. Mathematical economics emphasises mathematical modelling of economic and financial markets. Big Data Business Analytics deals with large and complex data from widely different sources for the use in economics and business. The specialisation depends upon the electives and Master’s courses chosen; a flexible mixture of these four specialisations is also possible.
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Econometrics
Course - Professional educationAfter this course, the student should be able to:
– Translate economics and business questions into econometric models and hypotheses;
– Analyze discrete choice data, panel data, time series;
– Interpret the conclusions properly, and understand the role of assumptions;
– Use the statistical programming language R for econometric model building.
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Econometrics 1
Course - Bachelor (University)In this course the multiple regression model is developed with applications, in particular, to cross sectional data. The treatment makes extensive use of matrix algebra and multivariate statistical theory. Discussed are: the classic linear regression model, standard assumptions, properties of the LS estimators, fit, consequences of omitted or redundant variables, partial regression, multicollinearity, linear restrictions, prediction, asymptotic properties and variable transformations, dummy variables, test for parameter stability, test for normality, heteroscedasticity, serial correlation, endogeneity of explanatory variables and instrumental variables. Applications and simulations are carried out with the software packages EViews and R or Python.
Minor Actuarial Science
Minor Econometrics and Mathematical Economics
Bachelor’s Actuarial Science
Minor Actuarial Science
Bachelor’s Econometrics
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UvA -
Econometrics 2
Course - Bachelor (University)The course explains a number of fundamental concepts that are important for the interpretation of quantitative results: including measurement errors, simultaneous equation bias, self-selection, censoring, truncation, etc. These ideas are fairly easy to explain with econometric models. Using these models, tests can be derived and solutions can be found for the problems involved.
Modeling itself is also an important skill and the course provides some initial techniques and extensions for correct modeling of economic variables. We will consider:
- non-linear methods including NLS and Maximum Likelihood;
- models with endogenous regressors (simultaneous equations, IV, GMM);
- models for discrete choices and other limited dependent variables (including Logit, Probit, Tobit);
- estimation and testing of the above models in practice using the computer;
- correct application of techniques and methods suitable for econometric research and the correct reporting thereof.
Minor Actuarial Science
Minor Econometrics and Mathematical Economics
Bachelor’s Actuarial Science
Minor Actuarial Science
Bachelor’s Econometrics
UvA -
Econometrics and Data Science
Programme - MasterThe Bachelor Econometrics and Data Science (EDS) specialization at the VU Campus in Amsterdam will offer you an inspiring and challenging study that is ideal for today’s digital world. EDS is internationally-oriented, fully taught in English, and will provide you with the necessary theoretical knowledge to succeed as an econometrician and data scientist for:
– Companies providing services on the internet, such as Google, Amazon and Booking.com
– Banks, financial institutions and consultancies, such as ING, ABN Amro and Deloitte
– Economic decision centers, Research institutes, Central Banks and Governments
After having completed the Econometrics and Data Science Bachelor, you can become a specialist in analyzing complex economic and financial data. With your unique knowledge you can help shape the operations and marketing strategies of companies, the performances of banks, or the decisions of governments.
This specialization will help you to develop an integrated perspective to Econometrics and Data Science. You will learn to think analytically and gain a balanced overview of diverse and interconnected issues on collecting, analyzing, modeling and forecasting data. You will also learn how to visualize data and how to present your findings, results and conclusions. The Econometrics and Data Science study is a technical and hands-on study that will prepare you for any future!
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Econometrics and Operations Research
Programme - Bachelor (University)The bachelor programme Econometrics and Operations Research combines various disciplines such as mathematics, statistics, informatics and economics. The programme is both broad and specific. Because of your knowledge of econometrics and practical skills, you will be able to provide solutions for issues in the field of economics. Your models for example will be able to determine the effects on employment of an ECB-initiated interest rate decrease, show the risk scenarios of a specific investment strategy, or help create the optimal planning for the deployment of trains. Econometrics and Operations Research at the VU is a small-scale education programme. You can always count on the personal help of senior students and teachers, and high-quality education. This course is in Dutch.
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Econometrics and Operations Research
Programme - MasterThe Master’s programme in Econometrics and Operations Research is an academic programme focusing on the development and application of quantitative methods for analysing economic issues in a broad sense. The components of the Master’s programme correspond closely with the department’s research interests, which means that many of the latest scientific developments in areas like financial econometrics, logistics and game theory find their way directly into the teaching programme.
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Econometrics for Quantitative Risk Management I
Course - Bachelor (University)This is a course for the Duisenberg Honours Programme in
Quantitative Risk Management. It is accessible for outside students, if
they have sufficient background in probability, statistics, linear
algebra, econometrics and programming.The course starts out with the theory behind common estimation methods
for linear, non-linear, or even non-parametric models. This knowledge is
applied to the study of factor, principal component and panel data
models. The second part of the course continues with a focus on time
series models with time varying parameters, both in a univariate as in a
multivariate setting.
Students are required to implement some of the methods in case
assignments using computer coding. We use Python as our standard
programming language, but students are free to choose some other
language if they prefer. -
Econometrics for Quantitative Risk Management II
Course - Bachelor (University)This is a course for the Duisenberg Honours Programme in Quantitative
Risk Management.
Following part 1 of this course, we proceed with studying econometric
methods for time-series data. We start with univariate and multivariate
linear time series models. We study their estimation and inference
procedures. Next, we consider non-linear time series models, In
particular, we consider models for volatility and financial risk. We use
this as a stepping stone towards general non-linear time series models
with time-varying parameters. We close with an introduction to state
space models and non-stationary time series models and their
applications within finance.Students are required to implement the methods studies in case
assignments using computer coding. In this way, they develop hands-on
expertise with the methods and become familiar with both their potential
and their limitations.We use Python as our standard programming language, but students are
free to choose some other language if they prefer. -
Econometrics I
Course - Bachelor (University)Getting acquainted with the concepts, theory, methods and techniques
from econometrics. Most importantly, the introduction of regression,
testing and maximum likelihood will be covered.Topics include
– Simple linear regression
– Hypothesis testing
– Finite-sample and asymptotic properties
– Multiple regression and its matrix algebra
– Inference : estimation and testing
– Maximum likelihood -
Econometrics II
Course - Bachelor (University)Acquainting the student with misspecifications in the linear regression
model and extensions of the linear regression model.Topics include:
– Heteroskedasticity
– Instrumental variables and endogeneity
– Misspecification: non-linearity and dummy variables
– Regression models with time series data and serial correlation in the
errors
– Strict and contemporaneous exogeneity
– Binary data: logit/probit models
– Multinomial data: ordered logit/probit model, multinomial logit model.
– Censored/truncated data: tobit models
– Non-normality -
Econometrics III
Course - Bachelor (University)Econometrcis III provides an introduction to multivariate dynamic models
and time-series analysis. The course covers both theoretical and
practical aspects of time-series econometrics including analysis of
multivariate stationary and non-stationary processes, vector
autoregressive (VAR) models, vector error correction models (VECMs), and
cointegration tests. The course also introduces panel data models,
methods and techniques. -
Econometrics Research
Course - MasterThis selective master course places students in contact with some of the
latest research in econometrics and data science. Instead of reading
textbooks prepared for the classroom, the students will read recent
research articles, published in top international scientific journals.
The students will also be trained in writing and presenting econometric
research. -
Econometrics: Complexity and Economic Behaviour
Track - MasterThe Master of Science programme in Econometrics is a multi-disciplinary Master’s programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages – such as Eviews, R and Matlab – to explore and analyse problems in economics and finance.
Four specialisations are offered. Econometrics emphasises statistical techniques for micro- and macro- econometric analysis, whereas Financial econometrics focusses on mathematical and statistical techniques and their application to financial models and time series. Complexity and Economic Behaviour emphasises mathematical modelling of economic and financial markets. Data Science and Business Analytics deals with large and complex data from widely different sources for the use in economics and business. The specialisation depends upon the electives and Master’s courses chosen; a flexible mixture of these four specialisations is also possible.
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Empirical Economics
Course - Bachelor (University)The main goal of this course is to make students familiar with using microeconometric techniques to empirically analyze economic models. Students should be capable to test economic theories empirically and to estimate policy relevant parameters. Next they learn how to interpret estimation results and to translate these into policy conclusions. Students learn to distinguish between causality and correlation.
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Empirical Finance
Course - Bachelor (University)The objective of the course is to show how econometrics can be applied to empirical questions in finance. In particular the course will cover topics such financial data and its properties, testing pricing efficiency and factor models, modelling volatility, risk management, continuous time finance. A mixture of academic papers and practical applications is used to study how econometric methodology is employed to facilitate financial decision making and extract information from financial market data.
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Empirical Transport Economics
Course -This course aims to familiarize students with applied empirical
transport research and how to interpret recent applied work to evaluate
important transport policies. The course consists of lectures,
interesting home assignments and tutorials where assignments will be
discussed. In the lectures, we explain recent developments in empirical
strategies in transport research that are theoretically founded and
which help you to examine transport policies from a welfare perspective. -
Entrepreneurship
Course - Professional educationAfter this course, the student should be able to:
– To understand the core concepts and models of entrepreneurship in both new ventures and large existing companies (intrapreneurship);
– To analyse and understand key challenges of innovation and launching new digital products and services including innovations organize to execute issues within larger organizations;
– To analyse how companies execute techniques from the start-up and venture world;
– To collaborate in a team and create and present a new offering that solves a real business need in a complex organisation, including a business model;
– Via a case study of GE industrial internet, learn how the largest industrial company in the world is turning themselves into becoming the Digital Industrial Company;
– About the ’transition gap’ – the phase between ’lean start-up’ and ’crossing the chasm’, a critical phase which prevents some start-ups from growing to their full potential.
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Equational Programming
Course - Bachelor (University)In the practical work we use the functional programming language Haskell. We practice with the basics such as lists, recursion, data-types, a bit of monads. The theoretical part is concerned with the foundations of functional programming in the form of lambda-calculus and equational reasoning. We study in untyped lambda-calculus beta-reduction, reduction strategies, encoding of data-types, fixed point combinators and recursive functions. In addition we study the lambda-calculus with simple types, its typing system and a type inference algorithm. In equational reasoning we work towards the results that all initial models are equal up to isomorphism, and that the term model is an initial model.
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Ethics, Law and Privacy (for BADS)
Course - Professional educationThe process of data analysis consists of three phases: (i) data collection, (ii) querying the data and (iii) the consequences you draw from this analysis. Ethical and legal aspects play a role in all of these phases, and these will be discussed in this module.
Please note that this course cannot be followed separately.
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Finance
Course - Bachelor (University)The performance of a corporation depends on how well managers succeed increating shareholder value. We show you how to use tools that are offered by financial theory and help you just doing that: creating value. In this course we discuss three main issues in finance: capital budgeting, asset pricing and financial investments. The capital budgeting decision involves how firms select projects that create value. The theoretically optimal decision rule—th e net present value method—is discussed, also in relation to other selection criteria that are applied in practice. The asset pricing part concerns the way financial assets are priced by the market. The focus is on the pricing of shares issued by firms and bonds issued by firms and governments. Questions raised are: How are the term structure of interest rates and promised coupon payments related to bond prices? What is the influence of the expected stream of dividends and the level of market risk of firm’s projects on the price of shares? The financial investment decision is approached from a portfolio perspective and ends with a discussion of the Capital Asset Pricing Model (CAPM).
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Financial Accounting
Programme - Professional educationIn this course, we aim to explain why accounting is seen as the ‘language of business’. Even though healthcare organisations often are not aiming to maximize their profit, like any organisation they need to explain their activities in financial terms. The field of accounting provides you with the tools and techniques to do so. Although it does not come natural to everyone, understanding the logic behind a unit cost, a balance sheet, or a profit number, will make your life as a manager easier.
The course deals with accounting techniques. These are not specific to healthcare situations. We will frequently use examples from a healthcare setting, but there is no such thing as a costing or budgeting technique for healthcare which uses a different logic or is based on other principles than those used for determining financial results of a company or the unit cost of a product. Having said that, upon completion participants should be able to understand and explain why it is difficult to evaluate the performance of hospitals from a financial perspective, or why it is difficult to improve financing mechanisms such as DBCs, DOTs or ZZPs.
After completing this course, participants are able to do the following:
- explain what assets, liabilities and equity are;
- prepare simple financial statements and interpret complex financial statements;
- use accounting information in decision-making;
- understand and apply cost allocation mechanisms;
- describe budgeting processes and explain the uses of budgets;
- perform job costing and cost allocations;
- explain what performance management is.
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Financial Econometrics
Track - MasterThe Master of Science programme in Econometrics is a multi-disciplinary Master’s programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages such as Eviews, R and Matlab, to explore and analyse problems in economics and finance.
The Specialisation/Track Financial econometrics focuses on mathematical and statistical techniques and their application to financial models and time series. For this specialisation, the Bachelor’s course Mathematical and Empirical Finance must be a part of the Bachelor’s study programme or deficiency programme.
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Financial Econometrics
Course - MasterThis course will enable you to become acquainted with econometric techniques that have been developed for the analysis of financial markets. Furthermore, to be able to apply these techniques on empirical data and to interpret the results of such empirical analyses from a financial perspective.
Econometrics
Actuarial Science and Mathematical Finance
Economics and Business
UvA -
Fintech and Blockchain
Course - Professional educationUnique two-day masterclass introduces participants to digital currencies, emerging mobile payment systems and blockchains, by the top expert of the world professor David Yermack from NYU Stern Business School.
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Fintech: Blockchain & Cryptocurrencies
Course - Professional educationAfter this course, the student should be able to:
– Acquire an overview of digital currencies, blockchains, and distributed ledger technology;
– Learn about potential applications of distributed ledger technology to new products and services;
– Explore blockchain technology and its potential to provide faster, cheaper, and more secure financial transactions;
– Understand the opportunities and risks from smart contracts and other emerging technologies.
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Foundations of Computing and Concurrency
Track - MasterThis track aims at Computer Science students with a general interest in Computing and Concurrency and the application of formal methods for system design. Computing is a fundamental phenomenon in computer science and we provide courses addressing this field in a wide range: from distributed algorithms to protocol validation, and from term rewriting to logical verification. In order to enhance background knowledge and to support the further study of foundational questions some general courses in logic and mathematics are provided as well. Concurrency naturally occurs in the specification of distributed systems, and their analysis, verification and implementation require a systematic approach, aided by formal methods.
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Foundations of Multi-Agent Systems
Course - MasterThis courses focuses on the design and analysis of (multi) agent systems from a logical perspective. This course will be based on Michael Wooldridge’s book: An Introduction to Multi-Agent Systems (Wiley). The main topics include: goals and intentions, perception-action cycle, action planning, reasoning and search in agents, architectures for agent systems, collaborative agents, communication, and distributed problem solving. Focus will be on the logical approach to agent systems and on multi-agent settings. The main concepts will be applied to implementations. The course includes a practical part in which students implement agent systems and perform experiments.
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Fundamentals of Bioinformatics
Course - MasterInterested in Bioinformatics? Or you want to find out how biology can make an exciting application domain? Or you want to learn how what more you could do with your data, and with less effort? Enter here to start! Fundamentals of Bioinformatics (FoB) is the starting course of the Bioinformatics master. It aims to give a broad overview of important
topics relevant to the field, with a focus on current open problems. Students will be made aware of these open problems during practical sessions that aim to let the student ‘stumble upon’ these problems by themselves. Based on their background, students will be assigned to
separate classes where they will be working to fill gaps in their background knowledge in programming and/or biology. -
Fundamentals of Data Science
Course - MasterData science is a dynamic and fast growing interdisciplinary research field. Industry, governments and academia operates on large amounts of data. But how do we deal with such large amounts of data? Is there a general framework to gather, analyze, model and visualize the data? What techniques do we use? What are the legal and ethical aspects regarding these data sets? This course will introduce methods for a number of key aspects of data science: data gathering, data analysis, data visualization and ethical and privacy issues. During the course, you work in a small team of students on a series of three projects that bind together all elements of the data science process; from formulating a research question, gathering data, exploring the data, modeling the data and communicating and visualizing the results. The data sets are from external companies and organizations within the following domains: (1) politics, (2) marketing and branding, (3) health. We will be using Python for all programming assignments and projects.
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Fundamentals of Data Science in Medicine
Course - MasterData analytics is fast growing in medicine and health care processes. In this module, we lay out the fundamentals and practice of this kind of research: statistics (parametric methods), machine learning (non-parametric methods) and design methods.
In this module some traditional parametric and newer non-parametric analytical techniques will be studied, discussed, and applied. We aim that students are capable of performing analytical research with complex data in an autonomous manner covering the phases of: data collection, cleansing, analysis, visualization, interpretation of results, etc. In particular, this module equips the student with conceptual and practical tools to understand essential issues in medical data science. These issues concern, among others, the systematic and structured approach to perform data research; representation of the data; the choice of an appropriate analysis technique; the assumptions made by the chosen approach; and the effective visualization of the results.
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Green Lab
Course - MasterLearn the basics of empirical experimentation in the field of Software Engineering. Be able to operate in a lab environment and build a successful experiment for software energy consumption. Become familiar with the research problems in the field of green software engineering. Understand and measure the impact of software over energy consumption.
Students will work in teams to perform experiments on software energy consumption in a controlled environment. They will have to carry out all the phases of empirical experimentation, from experiment design to operation, data analysis and reporting. They will be provided with examples of previous experiments, but they will have to choose by themselves the experimental subjects and hypotheses to test. During the lab sessions, students will be assisted for technical operation of the lab equipment as regards measurement and data gathering. Students will also receive the required training for data analysis and visualization (i.e. graphs, dashboards) using specialized software.
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HBO-ICT – Information and Communication Technologies
Programme - Bachelor (HBO)It is hard to think about or society without also thinking about information and communication technologies (ICT). As a result the ways in which people and companies communicate with each other is rapidly changing. If you want to work in ICT, HBO-ICT is the bachelor programme for you. In four years you will become an ICT professional that is able to make a difference. For example as a mobile app developer, game developer, IT security specialist, telerobotics researcher, IT business analyst, or as an entrepreneur in the ICT sector. This course is in Dutch.
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High Performance Computing and Big Data
Programme - MasterObjectives
- Identify the appropriate HPC methods and tools to scale up scientific applications
- learn to use Big Data Methods, techniques, and tools to solve data intensive applications
- Develop skills to use HPC, and Cloud facilities
Contents
The course covers a number of key topics in the field of high performance computing and big data engineering. The course is organized as a lectures and workshops which help the students to develop both theoretical and practical skills. Following is the list of topics covers in the course:
- Introduction to parallel programming models and Big Data
- Grid/Cloud Computing
- General-purpose graphics programing unit for Big Data Application
- Big data processing: Apache Spark and storm
- Relation BD and NoSQL, NewSQL
- Data Intensive computing with Hadoop: MapReduce and Pig,
- Local/Remote Visualisation for Data intensive application
- HPC Cloud.
Students must be able to program in Python, Java, and basic C (or be able to get the needed skills on the fly).
UvA -
High Performance Computing and Big Data
Course - MasterStudents will develop skills in High Performance Computing which are commonly used to solve Data intensive applications and avoid common pitfalls which often lead to misuse of valuable computing resources. In this course students will learn about: approaches used in HPC and distributed computing; methods and techniques to solve Big Data problems; and to develop skills to use HPC facilities and e-infrastructure.
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High Performance Computing and Big Data
Course - Professional educationResearchers and engineers from industry and academia alike frequently experience their daily work to be impeded by physical limitations of their ICT equipment: processing and storage capacity, visualization facilities and their integration. They feel that up-scaling to high performance computing (HPC) facilities would be very beneficial to their work, but don’t know how to do this and lack the time to investigate their options.
The objective of this course is to introduce individuals with limited programming knowledge to various HPC facilities. At the end of the course, they will be able to use them avoiding common pitfalls, thus saving them money and time. The course is composed of a number of independent modules touching on various HPC and Big Data issues: Introduction to Unix, distributed systems, and Big Data; Using state-of-the-art Super Computers (with hands-on on the National Super Computer Cartesius and the Lisa cluster); HPC Cloud; GPU programming; Local and Remote Visualization Techniques; Data management; Data Intensive Computing with Hadoop: MapReduce and Pig; MPI/OpenMP approaches used in HPC and distributed computing.
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ICT4D: Information and Communication Technology for Development
Course - MasterIn the developed world computers are ubiquitous, and ICT has rapidly grown into a critical asset for economic, technological, scientific and societal progress. The main objectives of this course are:
To make the next generation of Computer Scientists aware of:
a) The importance of ICTs for the developing world and the unexpected way developing countries are leapfrogging into the information age
b) The opportunities and challenges that exist for an information scientist in the area of ‘development4development’
c) The influence of context in a typical ICT4D project
d) The complexity of deploying an ICT project within a development context, and how to tackle this.To equip the students with some initial project management, technological and programming skills specific to an ICT deployment in a developing country. Positioned at the heart of the VU’s vision of social relevance as one of the guiding principles, the core aim of the course is to raise the awareness that we as Computer Scientists can make a significant difference by sharing our expertise according to well established principles of international development.
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Information Management
Track - MasterNo organization can do without information systems. For some organizations, such systems are even of strategic relevance, as they offer a clear competitive advantage. Think, for example, of how Amazon has become such a dominant retailer or how an organization as Uber has conquered the taxi market. This course explains the relevance and use of information systems in modern organizations. We will briefly sketch how the role of information systems has developed over the years to reach its current ubiquitous level. Special attention is devoted to the rise of the internet and its impact on traditional organizations, as well as the emergence of new types of (cloud-based) organizations. Reasoning from the organizational importance of information systems, we will look into the way information systems are developed such that organizations can achieve their objectives. We will pay considerable attention to an important phase in information system development, namely how we analyze and model business processes. For this purpose, we will rely on the use of classical Petri nets. This course will approach the topic of information management in breadth and in depth. Breadth is achieved by giving an overview of all relevant topics in the area of information management; depth is attained by introducing students to a powerful, formal modeling technique that they will learn to master in the context of organizational analysis.
Business Management
Economics
Economics and Business
VU -
Information Retrieval
Course - Bachelor (University)In this course you will learn how search engines and other information retrieval systems work, to understand the principles and methods, and to acquire some basic skills in programming important aspects of such systems.
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Information Retrieval 1
Course - MasterThe underlying question behind this course is: How do search engines work? To answer this question we dive into the details of information retrieval, the field that deals with search. During the course we discuss the various parts of search engines:
– Retrieval models: how do we retrieve relevant documents for a given query? And how do we rank these documents in the right order?
– Evaluation: given a working retrieval system, how do we determine its performance and how can we compare it to other systems?
Besides these two basics of information retrieval we explore other frequently used techniques, theories, and models (e.g., relevance feedback, learning to rank, and semantic search).
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Information Retrieval 2
Course - MasterBuilding on the Information Retrieval course (MSc AI, term 3), this course will focus on the design, execution, and analysis of state-of-the-art retrieval methods. Over the years, IR ranking functions have come to include features based on content-based analysis, based on the analysis structure (page, site, link), and based on user behavior. Now that IR systems need to consider ever more heterogenous information sources (e.g., on the web, social media, etc.) to address more diverse user needs and expectations, new ways of integrating dozens or even hundreds of ranking criteria are developed. In this course we discuss new learning to rank models and approaches, and particularly focus on online models and their evaluation.
Topics include learning to rank models, click models, experimental design, social media search, recommendation algorithms.
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Information Risk Management
Course - Bachelor (University)This course aims to provide students with an understanding of how internal control has to be designed to realize reliable management information from a risk management perspective. Additionally the application of ICT technology within organizations is of great influence on the internal control of organizations. The formal organizational structure, segregation of duties and procedures can lose their importance if not completely redundant. The use of IT has consequences for the effectiveness and efficiency of the internal control and risk management systems of the organizations concerned. ICT requires continuous coordination between business objectives, governance, risks and compliance.
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Information Science
Programme - Bachelor (University)Information Science is about the development and potential applications of modern information technology, as well as its effects on people, society and business. Studying Information Science, you will learn how people process information, communicate, use technical resources, and how new media such as the Internet or mobile technology can support this. Besides learning how people process information, communicate and use technical resources, you will learn from a management studies perspective about how important information is to businesses, how information products are marketed, and how ICT investments of millions of euros are managed. An understanding of technology is necessary for understanding what is possible, and what is not.
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Information Sciences
Programme - MasterInformation Sciences is the multidisciplinary area bridging Information and Communication Technology (ICT) and its practical use in society. Are you interested in how information is created and processed in companies and institutions? Are you more interested in the application of technology than technology for its own sake? Do you believe it’s important not to lose sight of the role people, organisations and cultures play in designing, modelling, communicating and sharing information? Are you fascinated by knowledge and innovation? If so, then the Master’s programme in Information Sciences at VU Amsterdam is an excellent choice for you.
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Information Studies
Programme - MasterInformation studies is a broad and interdisciplinary field, primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information. It examines the interaction between people, organisations and any existing information systems, with the aim of creating, replacing, improving or understanding information systems. Information studies tackles systemic problems first rather than individual pieces of technology within that system: it focuses on understanding information problems from the perspective of the stakeholders involved, and then applying technologies as needed. Not only aspects of computer science are incorporated, but also aspects of research fields like cognitive science, commerce, communications, management, philosophy, public policy, and the social sciences. The Master’s programme in Information Studies at the UvA offers specialisations in: Data Science; Game Studies; Information Systems
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Information Systems
Track - MasterInformation Systems is the Master’s programme for you if:
– you are interested in the ways people interact with (new) technology and media, and how they are supported, hampered and influenced by them
– you want to analyse systems for the supply, storage and communication of information by means of various media (such as video, speech and text)
– you want to make connections between corporate/organisational management and the people responsible for developing technological solutions
– you want to translate user demands into innovative solutions
Information Studies
UvA -
Information Theory
Course - MasterIn this course, we quickly review the basics of probability theory and introduce concepts such as (conditional) Shannon entropy, mutual information and entropy diagrams. Then, we prove Shannon’s theorems about data compression and channel coding. An interesting connection with graph theory is made in the setting of zero-error information theory. We also cover some aspects of information-theoretic security such as perfectly secure encryption.
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Information Visualization
Course - MasterGaining insight into large collections of data requires an intricate interplay between data analysis, data mining, domain knowledge, visualization, and interacting users. In this course we will study the development of methodologies which support the process of gaining insight in large and complex datasets by a combination of data analysis, machine learning, and information visualization. Methods are geared towards designing and realizing information visualizations which, in an optimal way, support the insight gaining process.
Artificial Intelligence
Forensic Science
Information Studies
Computer Science (Joint Degree)
UvA -
Intelligent Systems
Course - Bachelor (University)This course gives an overview over the theory and practice of Intelligent Systems, systems that perceive, reason, learn, and act intelligently. Students will acquire practical skills in developing intelligent systems building on a thorough understanding of well-understood Artificial Intelligence approaches, including Knowledge Representation and Machine Learning.
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International Study Trip: Entrepreneurship and Innovation in Silicon Valley
Course - Professional educationOur international study trip is your chance to intensify your outlook on international business. During this exciting 1-week trip you take lectures at a top business school, visit local companies and networking events. During the study trip you will meet industry experts, executives, entrepreneurs and consumers in a classroom setting, but more importantly in real-life corporate settings.
In the last few years the destination of the International Study Trip was Silicon Valley, California, U.S., home of companies that have built their success on big data, like Google, eBay, Facebook and Apple. The programme offered insight into the nature of business in this leading hub for high-tech innovation and development. Students got the unique opportunity to personally experience the transformative power of big data and learn from real life cases.
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Internet and Web Technology
Track - MasterInternet and the World Wide Web play a central role in our society, and have changed the way software systems are engineered and provisioned. Recent advances in virtualization techniques as well as the emergence of Software-as-a Service (SaaS) and cloud-based paradigms have enabled new ways of providing and exploiting computing and IT resources over the Internet. This track aims specifically at preparing students to work in such a complex, dynamic and distributed environment. It gives both in-depth understanding of the key components in developing distributed software- and service-based systems over the Internet, and provide the students with technical and critical thinking skills for the design and performance evaluation of such systems.
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InterNetworking and Routing
Course - MasterPlease refer to the System and Network Engineering web pages for detailed and current course information.
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Internship Minor – Applied Econometrics: A big data experience for all
Minor - Bachelor (University)The Minor Applied Econometrics provides a thorough introduction to econometric methods and techniques with an emphasis on how to implement and carry out the methods in empirical studies and how to interpret the results. The key steps of model formulation, parameter estimation, diagnostic checking, hypothesis testing, model selection and empirical analysis are given extensive attention throughout the different courses.
Apart from the fundamentals of econometrics, much emphasis is given to how econometric methods are carried out in different empirical settings and studies. Particular attention will be given to issues related to “big data” in the context of different disciplines in economics and business. The students are given some flexibility to opt for a specialization/track in economics, finance or marketing; one may label such specializations as “Minor in Applied Econometrics”, “Minor in Financial Econometrics”, “Minor in Quantitative Marketing”, etc. It will allow the student to focus on a subject of their own liking.
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Introduction Econometrics and Actuarial Science
Course - Bachelor (University)This course consists of two Orientation segments and a Skills segment.
Orientation segment Econometrics:
- the student will get acquainted with the basics of econometrics and after successful completion of the course will be able to explain basic concepts of econometrics such as linear regression, testing of parameters, unbiasedness, consistency, and efficiency;
- after successful completion of the course, the student will be able to carry out both explorative analyses of economic data using simple statistical models and analyses of economic relations independently.
The student will also learn how to implement calculations and estimations during computer classes.
Orientation Actuarial Science
This segment is designed to offer students a notion about general topics in the fields of Actuarial Science, Mathematical Finance and Quantitative Risk Management. More detailed learning objectives for this segment are:
- basic knowledge of Life Insurance Mathematics and Financial Mathematics, such as the equivalence principle for cash-flows, calculations of premiums and technical provisions for simple life-insurances, and option pricing by replication and change of measure;
- Risk Theory and Risk Management, such as an analysis of the implications of the Law of Large Numbers, and analysis of premium and risk capital calculations for catastrophe and motor insurance.
The student will also learn how to implement calculations in the aforementioned fields using MS Excel and R during the computer lab sessions.
Skills segment:
The students will be able to:
- design and conduct an empirical research project;
- analyse and judge the results of this research;
- write a report about their research.
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Introduction to Business Analytics
Course - Bachelor (University)In this course students get an understanding of the contents and the objectives of the Business Analytics curriculum. There are lectures on relevant aspects of business administration, and through 2 cases students learn to see the connections between the different scientific fields. Also computer and communication skills are part of the course.
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Introduction to Computer Vision
Course - Bachelor (University)Bij zien komt heel wat kijken! Visuele taken die de mens letterlijk zonder nadenken uitvoert (omdat ze in onze hardware zitten) kunnen we computationeel duiden en expliciet implementeren.
In deze cursus converteren we digitale beelden gebaseerd op pixels naar ‘visuele kenmerken’ (features) voor latere verdere verwerking in tracking, classificatie, of 3D-reconstructie. Voor die overgang gebruiken we wiskundige modellen van de lokale structuur en kleur van beelden, het menselijke visuele systeem, het afbeeldingsproces van de camera, en computationeel efficiënte algoritmen. Die modellen zijn de basis van computer vision, het zien met de computer! We slaan een brug tussen deze basis modellen en meer geavanceerde technieken zoals het categoriseren van plaatjes met convolutional-neural-nets.
Computer vision maakt gebruik van lineaire algebra en Taylor-reeksen van multi-dimensionale functies (de plaatjes), en heeft als doel deze te implementeren op de computer. In dit vak ga je dus het vak Lineaire algebra en het vak Continue wiskunde en Statistiek (met name de calculus) gebruiken en je moet kunnen Programmeren.
- Low level vision (interpolation, warping, local operators, convolutions),
- Local structure in images, Scale-Space, Feature Detection (SIFT),
- Pinhole camera, Camera calibration, Stereo Vision
- Motion, Optic flow, Tracking (als het blokschema dit toelaat)
- Convolutional Neural Networks voor computer visie
This course is avaliable only in Dutch
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Introduction to Econometrics
Course - Bachelor (University)This course in the minor Applied Econometrics is targeted at non-econometrics students. By the end of this course students will have had an introduction to modern econometric techniques, that will enable them to conduct methodological or empirical analyses of their own. In particular, students will be familiar with both econometric theory and with real-world applications in macroeconomics, finance and business.
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Introduction to Econometrics, Operations Research and Mathematical Economics
Course - Bachelor (University)Econometrics:
The goal of econometrics is to describe the relations between
observations as a useful model, from which predictions and inference can
be made. This introduction presents some first filters which can be used
for a
predictions, and continues with the principles of regression, applied to
real data.Operations Research:
Core business in Operations Research is optimization. Several problems
from network optimisation are covered, among which the shortest path
problem, the minimal spanning tree problem, and the maximum flow
problem. For these problems, the mathematical structure is studied
leading to the design of algorithms for solving them. A glimpse will be
offered on complexity theory by analyzing the computation time of these
algorithms theoretically.Mathematical Economics:
Within economic science frequent use is made of mathematical models.
Many of these models try to explain the choices of economic agents in
their economic environment. In this introduction, we set the first steps
in modelling mathematically the decision processes in economics, looking
both at behaviour surrounding individual choice in strategic decision
situations, and at cooperative decision making. -
Introduction to Matlab Programming for Neuroscientists
Course - MasterThe course will comprise a basic introduction to MATLAB, designed to be suitable for students with little or no prior programming knowledge. The program will be based on introductory lectures and hand-on assignments, with the help and supervision of expert programmers. During the first half of the course the students will be guided to program their first tool to acquire actual neuroscientific data. In the second half students come up with a neuroscientific research question themselves. They will use their new knowledge to create a tool to acquire data to answer this question. Additionally, there will be introductory lectures on so called tool-boxes commonly used for data acquisition and analysis in neuroscience research.
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Introduction to Programming (Java)
Course - Bachelor (University)This course teaches the use of computers to solve problems with algorithms and structured programming. The course content includes: primitive types, declaration, expression, assignment statement, iterations, methods, I/O using PrintStream and Scanner, array, class, object, standard classes String and Maths, design of programs, matrix, using several self made objects in a program, recursion and using a graphical interface through a pre-programmed package.
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Introduction to Programming (PYTHON)
Course - Bachelor (University)During this course, students learn to write program in Python using
types (int, boolean, float, list and str), expressions, assignment
statements, if-statements, iterations (while- and for-statement), They
also learn standard functions, module math, as well as how to make
functions, perform I/O, make classes and use objects. -
Introduction to Systems Biology
Course - MasterIntroduction to Systems Biology is the starting course of the Bioinformatics and Systems Biology master (together with Fundamentals of Bioinformatics).
Goals:
– To make the student acquainted with the major approaches and methodology in systems biology (to be studied in more detail in the master).
– To develop a basic understanding of biological concepts that are relevant to current topics in systems biology.
– To gain hands-on experience in basic modelling as a means of solving systems biology problems.
– To repair gaps in background knowledge. -
Introduction to Time Series
Course - Bachelor (University)This course covers both theoretical and practical aspects of time series econometrics including the analysis of stationary and non-stationary stochastic processes in economics and finance. The students are introduced to autoregressive moving average (ARMA) models, autoregressive distributed lag (ADL) models, and error correction models (ECM).
Furthermore, the course provides both theoretical and practical insight into parameter estimation in time-series and the use of these models for forecasting, testing for Granger causality, and performing policy analysis using impulse response functions.
Finally, the students are introduced to the fundamental problem of spurious regression in time-series analysis. We find a solution to this problem by taking a journey into the theory and practice behind unit-root test, cointegration tests and error-correction representation theorems.
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Introduction to Time Series and Dynamic Econometrics
Course - Bachelor (University)This course covers both theoretical and practical aspects of time series
econometrics including the analysis of stationary and non-stationary
stochastic processes in economics, business and finance.The students are introduced to autoregressive moving average (ARMA)
models, autoregressive distributed lag (ADL) models, and error
correction models (ECM). Furthermore, the course provides both
theoretical and practical insight into parameter estimation in
time-series and the use of these models for forecasting, testing for
Granger causality, and performing policy analysis using impulse response
functions.Finally, students become familiar with the fundamental problem of
spurious regression in time-series analysis. We find a solution to this
problem by taking a journey into the theory and practice behind
unit-root tests, cointegration tests and error-correction representation
theorems. -
Introductory Econometrics for Business and Economics
Course - Bachelor (University)A review will be given of estimation and testing in the linear
cross-sectional regression model. We will discuss the classical
assumptions, and the consequences arising when these assumptions are not
fulfilled. Throughout the course, the focus will lie on developing an
intuition for state-of-the-art econometric concepts. A balance will be
struck between theoretical derivations and empirical applications. The
textbook used (see below) is particularly well-suited for this purpose,
as it is targeted at an audience of advanced undergraduate students in
economics and business studies. Extensive use will be made of the
statistical software R, both for in-class illustration and for hands-on
exercises. -
IT Infrastructures
Course - MBAIn dit vak wordt ingegaan op de IT-infrastructuren die ten grondslag liggen aan onze moderne informatiesystemen. Aan de orde komen onder meer de eigenschappen van moderne datacenters, datacommunicatie en protocolfamilies als TCP/IP, dataopslagtechnieken, veel gebruikte platformen zoals Unix en Windows, cloud- en virtualisatietechnieken voor servers en werkplekken, database managementsystemen, middleware concepten als Service Oriented Architectures en messaging bussen en de integratie tussen de IT infrastructuur en applicaties. Beveiliging krijgt hierbij speciale aandacht.
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Knowledge and Data
Course - Bachelor (University)The objective of the Semantic Web course is to make students acquainted with methods and technologies used for expressing knowledge and data on the Web. At the end of this course, students will have built an intelligent web application that queries and reasons over integrated knowledge from various sources obtained from the Web.
Event though content on the web is generally produced from structured data sources (databases), its representation is in a form that is meant for human consumption. Linked Data allows to scale the walls of this siloed information space, by reusing identifiers and vocabularies across these datasets, and presenting that information in a way that is appropriate for machine consumption. Google, Bing and Yahoo already use this type of linked, structured information to improve web search and information retrieval. But it also helps content providers, such as the BBC, to better augment their content with content from other sources (e.g. from Musicbrainz).
In this course we will introduce the technologies and representation formats (RDF, RDFS, OWL) for expressing semantics and linked data in a web-accessible format, use the SPARQL query language to query over this data, and build a Web application that uses the data for some intelligent task.
Artificial Intelligence (Minor)
Web Services and Data (Minor)
Flexible Minor
VU -
Knowledge and Media
Course - MasterThe goal of the course is to provide insights in the concepts of information organization, knowledge representation, ontologies, and knowledge processes in relation to various ICT-based media. This course treats the principles and theories that form the foundation of information organization and knowledge-intensive processes, and puts them in relation to various media applications. Knowledge processes are those processes that use knowledge (reasoning), document knowledge (representation), acquire knowledge or transfer knowledge (teaching). The relation between knowledge processes and media will be explored, and various types of applications will be discussed.
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Knowledge Engineering
Course - MasterKnowledge Engineering is a discipline that involves integrating knowledge into a program for solving a complex problem, which requires human expertise. Typical tasks are classification, diagnosis, planning etc. In the course we use CommonKADS as the methodology for the process of modeling the organisation, the context and the knowledge intensive tasks. This methodology give clear guidelines and concrete templates for modeling the organisational aspects and the expertise model, which is the core model of knowledge based system. The notion of pattern-based knowledge modeling is a key issue in the knowledge modeling process. The goal of the final project is to perform the entire knowledge technology process for a knowledge intensive problem of your own choice, starting with context analysis, up to a (partial) implementation of the knowledge based system.
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Knowledge Representation
Course - MasterSince its early days the question of how to represent knowledge and how to reason with it, has played a central role in Artificial Intelligence. The aim of the course is to make students familiar with a number of important knowledge representation formalisms. For each formalism, the course will discuss (i) the representational form (ii) an inference mechanism, and (iii) an example application problem on which to apply both representation and inference. Students will be asked to perform computational experiments with each of the formalisms.
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Knowledge Representation on the Web
Course - MasterIn this course, you will learn the theory of knowledge representation languages that are used to express information on the Web, their application to real-world problems and data, and the research methods behind them.
Artificial Intelligence
Computational Science (Joint Degree)
Logic
VU -
Language Technology
Course - Professional educationAfter completing this course, students should be able to:
- Understand some of the most prominent language technologies, in particular:
- Text representation for machine learning (tf-idf, lsi, nmf, lda)
- Neural methods for machine learning (word2vec, doc2vec, rnn)
- Information retrieval
- Questions-answering
- Information extraction and knowledge graphs
- Understand the possibilities language technology offers
- Envision applications of language technology
- Apply the techniques in use-cases
- Understand some of the most prominent language technologies, in particular:
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Large Scale Data Engineering
Course - MasterThis course confronts the students with some data management tasks, where the challenge is that the mere size of this data causes naive solutions, and/or solutions that work only on a single machine, to stop being practical. Solving such tasks requires the computer scientist to have insight in the main factors that underlie algorithm performance (data access patterns, hardware latency/bandwidth), as well as possess certain skills and experience in managing large-scale computing infrastructure. Apart from the data being of large volume, another problem invariably is that data comes in strange forms and formats, is polluted, and needs to be transformed and cleaned. The main part of the course is the second assignment: a large big data analysis project where each student teams tackles a different problem, and while doing so gains experience in multiple aspects of large-scale data engineering (critical thinking, data management technologies, visualization techniques, paper writing).
More information is found on http://event.cwi.nl/lsde – also check the “showcase” section where you can see past project results (visualizations and papers).
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Law & Ethics for Big Data
Course - MasterAfter completing this course, students should be able to:
- understand the (international) principles and values concerning privacy and (personal) data protection;
- understand the basics of the EU General Data Protection Regulation (GDPR) and related EU-legislation;
- be aware of the possible impact, threats and risks of big data for privacy;
- understand that not all data may be necessary to use and that technologies (such as anonymisation and pseudonymisation) of data can help avoid or minimise the impact, risks and threats for the data subjects and the parties processing the data;
- be aware of the available methods and standards to design privacy-friendly systems and services (Privacy by Design);
- understand the (international) principles and values concerning use of AI;
- understand the OECD and EC guidelines on using Artificial Intelligence;
- understand related concepts of transparancy, explainability and fariness of AI;
- understand how these concepts can be ensured in the context of organisational use of data and AI, applying a legal and ethical perspective to data and AI in real-life cases;
- form a view/opinion of an organisation, a customer and a government/regulator on the use of (big) data and AI.
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Law & Ethics for Big Data
Course - MBAAfter this course, the student should be able to: Understand principles of privacy and data protection; Identify the possible risks of Big Data for privacy; Perform a law/ethics compliance scan; Understand technologies to minimize privacy risks; Design privacy-friendly systems and services; Understand that proper communication and transparency is key.
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Law and Ethics on Robots and Artificial Intelligence
Course - Bachelor (University)Amongst emerging technologies robotics and artificial intelligence are
prominent both in terms of existing as well as expected use in society.
These technologies are special, because they come close to how we humans
function. At this moment both robots and artificial intelligence are
primarily used for specific tasks (playing games, surgery, self-driving
cars), but developments are moving fast. What exactly the future brings
is difficult to tell, but no one denies the potential and risks related
to robotics and artificial intelligence. Not surprisingly, in the legal
and policy arena an active discussion is going on related to legal and
ethical issues. These are the issues addressed in this course. The
legal angle includes both existing law and the need for new law. If new
law is needed, discussion will also be on how this new law should be
drafted. For instance, presently the European Parliament is analyzing if
maybe some time in the future we may need some sort of legal personality
for robots, and Harari is even fantasying about legal personhood for
algorithms. Ethics can apply to both the development and use of robots
and artificial intelligence. In this course ethics is primarily used to
either constrain the application of existing law or to guide the
drafting of new law.Applications that are covered in this course include softbots, the
internet of (robot) things, ambient technology, autonomous intelligent
vehicles, and social robots (care and sex). -
Leading People Strategically
Course - Professional educationPeople are central to the success of organizations, but they are also the most difficult asset to manage. Therefore, managers require a strong understanding of how to lead and manage individuals and groups in order to effectively solve organizational problems and maximize long term performance. As a manager, you need to develop the skills and talents of employees, and motivate them to achieve strategic goals. You also need to effectively combine employees in teams, enhance collaboration and reduce conflicts. In addition, you help to build, strengthen, or change the organizational culture. You need to make things happen, and often under challenging and changing conditions or timeframes. In order to do this successfully, managers need to be able to diagnose and analyze problems, make effective evidence-based decisions, influence and motivate others, and manage their teams. This course aims to prepare you to make more effective managerial decisions and increase your impact as a manager.
This course is an introduction to the key elements of leading and managing people and focuses on frameworks, theories and tools that help to understand how to lead and manage people effectively. At the end of this course, students are expected to be able to:
Understand and explain important theories, frameworks, and research evidence related to leading and managing people effectively
Apply these theories, frameworks, and research evidence to people-related business problems
Diagnose and analyze problems, and make effective evidence-based decisions as a manager -
Learning Machines
Course - Bachelor (University)This course concerns robots that can adjust and improve their behaviour
over time.The course has a strong hands-on flavour. After two introductory
lectures students have to develop and implement the learning method of
their choice in simulation. In particular, adequate robot controllers
have to be learned autonomously for two tasks, maze navigation and food
collection. After testing and tuning the methods in simulation, the best
learned robot controller must be ported to a real Thymio and the real
world performance compared with that observed in simulation. -
Life Sciences: Bioinformatics and Systems Biology
Programme - MasterVast amounts of data have been collected through genomics initiatives. They provide a golden opportunity to research the secrets of life, to understand more of its complexities, to improve quality of life and to conquer major diseases. Converting this huge volume of data into real understanding is the basic challenge of Bioinformatics research.
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Linear Algebra
Course - Bachelor (University)After successfully completing this course, the student
– has a working knowledge of the concepts of matrix algebra and finite-dimensional linear algebra, such as echelon form, LU-decomposition, linear independence and determinants;
– is familiar with the general theory of finite-dimensional vector spaces, in particular with the concepts of basis and dimension;
– is familiar with the concepts of eigenvalues and eigenvectors, diagonalization and singular value decomposition and can apply these concepts in basic applications in
discrete time dynamical systems;
– has working knowledge of the concepts of inner product spaces and matrices acting in inner product spaces, including orthogonal projections and diagonalization of symmetric matrices. -
Logic
Programme - MasterLogic is an interdisciplinary and international two-year Master’s programme at the University of Amsterdam (UvA) that focuses on the central role of logic as a mediator between the sciences and the humanities. It is an incredibly complex and fascinating programme, intended for students who want to relate traditional fundamental research in the formal sciences to a wide variety of applications, ranging from Information Sciences to Linguistics and Philosophy. It is a programme for highly motivated students from all over the world, who are able to work in a inspiring and demanding environment, both individually and in groups.
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Logic and Modelling
Course - Bachelor (University)The course objective is to obtain a good knowledge and understanding of the most important logical systems: propositional logic, predicate logic and modal logic. The students learn to use these systems to model data, knowledge and actions. An important aspect of the course is the ability to reason using these logics and reason about these logics: what can and what can not be expressed with a logic system, and what are the differences between the systems with respect to expressive power or the existence of decision procedures.
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Logistics Analysis
Course - Bachelor (University)The course Logistics Analysis is an exciting course that will challenge you in various ways. By taking Logistics as a point of departure, we bring together several perspectives and analyze business problems faced by logistics companies. Taking a logistics perspective will stimulate you to think about organizations in a different way, bringing together knowledge from different fields and realizing that this creates challenges and conflicts that managers need to deal with. You will learn to systematically describe logistical systems, and identify problems that emerge in these systems. Moreover, this course offers you a number of tools that allow you to analyze logistical systems, optimize them, (re)design them and assess the consequences of suggested improvements. Important topics such as production management, inventory management, and maintenance management are addressed, which are essential, hands-on tools any logistics professional should be able to work with.
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Logistics Engineering
Programme - Bachelor (HBO)In the first year you will follow a general introduction in logistics. You will learn to produce what a client demands, how to distribute specific goods to a desired location, and you will learn what to buy to produce what a client demands. You will learn about the different disciplines involved in these processes, such as marketing, distribution logistics, production logistics and procurement logistics. Additionally, you will follow courses on mathematics, ICT, English, business administration and serious gaming. About a third of the programme will be given in a project-oriented format.
In the first year you will quickly get to know the logistics sector by doing project-based practical assignments and visiting companies in the field. In the second year you will further deepen your knowledge of logistics, such as setting up warehouses, managing the distribution of goods, tracking and tracing, and sustainable logistics. You will also go abroad on a study tour. In the third year you will follow a minor. You can – for example – choose to participate in the Logistics research programme, which focuses on Airport, Seaport or City Logistics. In the fourth year you will follow the final courses that will further sharpen your knowledge, and you will end the programme by doing a graduation assignment within a company. For ambitious students looking for an extra challenge we offer an additional education programme. This course is in Dutch.
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Machine Learning
Course - Professional educationAfter completing this course, students should be able to understand methods from machine learning, in particular: decision trees and decision forests; clustering and topic modelling; logistic regression and deep learning; matrix factorization; times series analysis and spatio-temporal event modelling. After completing this course, students should be able to apply the methods in advanced techniques: text analytics; image and video analytics; recommendation. apply the techniques in large scale use-cases.
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Machine Learning
Course - Bachelor (University)The goal of this course is to present the dominant concepts of machine learning methods including some theoretical background. We’ll cover established machine learning techniques such as Decision Trees, Neural Networks, Bayesian Learning, Instance-based Learning and Evolutionary Algorithms as well as some statistical techniques to assess and validate machine learning results.
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Machine Learning
Minor - Bachelor (University)Upon successful completion of this course, the students will be able to:
– Discriminate between different machine learning and pattern recognition methods, explain their main characteristics and choose an appropriate one for a given problem.
– Apply the methods on different types of data.
– Evaluate the performance of methods using different metrics.
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Machine Learning & Multivariate Statistics
Course -Most psychological research involves multivariate observations. This can be in the form of repeated measures or multiple dependent and independent variables. A host of multivariate procedures belong to behavioral scientist’s toolbox. These include the General Linear Model (GLM, including MAN(C)OVA and Repeated Measures analysis), Principal Components, Linear Discriminant Analysis, and clustering methods. In recent years, the power of these techniques has been highlighted in large scale application in Machine Learning and Data Science.
In the first part of this course we will focus on statistical inference using these techniques, and careful assessment of their underlying statistical assumptions. In the second part of the course we will contrast this use to Machine Learning applications of these techniques. Upon completion of this course, students will be able to (a) describe the most important machine learning and multivariate statistical techniques conceptually and mathematically (paraphrasing), (b) evaluate which method is most appropriate given a specific research question and the empirical data (evaluating, scientific thinking), (c) conduct data analysis using each of the machine learning and multivariate statistical techniques covered (scientific thinking, communicating, analysing), (d) compare and contrast machine learning methods with those used in multivariate statistics (evaluating, scientific thinking).
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Machine Learning 1
Course - MasterMachine learning is concerned with learning predictive algorithms from data. In this course you will learn about supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction). Special attention will be paid to statistically analyzing the results of applying an algorithm to a particular problem. You will learn the theory of machine learning in class and practice the theory during homework sessions. You will gain hands-on experience through a number of coding projects where you implement some of the algorithms.
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Machine Learning 2
Course - MasterGain an advanced level of understanding of the principles of machine learning;
Acquire the skills to apply machine learning to complex real world problems.
Use advanced machine learning techniques to analyze complex data and evaluate the resulting models.
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Machine Learning for Econometrics
Course - MasterThis course will be a mix of machine learning theory in regular lectures and application of this knowledge on large datasets in practical sessions. Topics include:difference and similarities of methods in econometrics and machine learning; linear classification models; neural networks and deep learning; kernel methods and support vector machines; graphical models; clustering methods; classification of images and text.
Econometrics
Actuarial Science and Mathematical Finance
Business and Economics
UvA -
Machine Learning for Econometrics and Data Science
Course - Bachelor (University)Machine learning originates from computer science and statistics with
the goal of exploring, studying, and developing learning systems,
methods, and algorithms that can improve their performance with learning
from data. This course is designed to provide students an introduction
to the main principles, algorithms, and applications of machine
learning. It includes topics related to supervised learning algorithms
for classification problems (logistic regression, support vector
machine), for regression problems (ridge regression, LASSO), but also
unsupervised learning algorithms (k-means, clustering, linear and
nonlinear dimensionality reduction). We adopt principles from
probability (Bayes rule, conditioning, expectations, independence),
linear algebra (vector and matrix operations, eigenvectors, SVD), and
calculus (gradients, Jacobians) to derive machine learning methods. We
further discuss machine learning principles such as model selection,
over-fitting, and under-fitting, and techniques such as cross-validation
and regularization. In case work we implement appropriate supervised and
unsupervised learning algorithms on real and synthetic data sets and
interpret the results. -
Machine Learning for Finance
Minor - Bachelor (University)With the increased availability of data and cheap and fast computing
power, analyses in many areas of human endeavour have become more and
more data driven. Finance is no exception. Applying machine learning
techniques to traditional finance questions might improve our
understanding.To date, applying these techniques has been the realm of IT savvy
researchers. With the increased availability of open source software
these techniques are becoming widely available. To fruitfully apply
them, however, finance professionals should get a much better grasp of
their strengths and weaknesses and this requires first hand experience.Topics covered so far in the Bachelor of Finance – or equivalent – are
of course many. For example, you will have seen derivative valuation
models and corporate finance topics. In this course we aim to revisit
some of these topics with the aim of solving them with open source
tools. This builds on the Python first principles course in the first
block. Then, once we have covered the basics, we will move to the more
advanced topics. This is difficult since there is so much to choose
from. Possible topics are:
– working with large dataset: database management
– high performance computing
– cooperation: GIT -
Machine Learning for NLP
Course - Bachelor (University)Machine learning is a dynamic and active research field. The main goal
of machine learning is to develop systems which can automatically solve
different problems without being specifically programmed, i.e. by
learning from the data. In this course, we will focus on the use of
machine learning as a methodology for solving NLP tasks (e.g.
pos-tagging, syntactic parsing, information extraction). We cover both
`traditional’ machine learning methods as the latest deep learning
approaches. Representation of language as data plays a prominent role in
this course.
Particular attention will be paid to the methodologies for using machine
learning in NLP research. We will cover the experimental setup, running
existing packages on new tasks and evaluation of overall results as well
as error analysis. The course covers practical skills that can be
useful in industry as well as in academia.
The course can be followed by any student with sufficient linguistic and
programming knowledge. -
Machine Learning for NLP (RM)
Course - MasterMachine learning is a dynamic and active research field. The main goal
of machine learning is to develop systems which can automatically solve
different problems without being specifically programmed, i.e. by
learning from the data. In this course, we will focus on the use of
machine learning as a methodology for solving NLP tasks (e.g.
pos-tagging, syntactic parsing, information extraction). We cover both
`traditional’ machine learning methods as the latest deep learning
approaches. Representation of language as data plays a prominent role in
this course.
Particular attention will be paid to the methodologies for using machine
learning in NLP research. We will cover the experimental setup, running
existing packages on new tasks and evaluation of overall results as well
as error analysis. The course covers practical skills that can be
useful in industry as well as in academia.
The course can be followed by any student with sufficient linguistic and
programming knowledge.
The course consists of two components: first basic machine learning
algorithms and how they are used for NLP are covered by theory and
practical assignments (6 ECTS, period 2). An additional 3 ECTS follow up
is offered in period 3 where the acquired skills are applied in a
practical assignment. -
Machine Learning for the Quantified Self
Course - MasterThe quantified-self refers to large-scale data collection of a user’s
behavior and context via a range of sensory devices, including smart
phones, smart watches, ambient sensors, etc. These measurements contain
a wealth of information that can be extracted by means of machine
learning techniques, for instance for the purpose of predictive
modeling. In addition, machine learning techniques can be a driver for
adaptive systems to support users in a personalized way based on the
aforementioned measurements. The type of data does however require
specialized machine learning techniques to fully exploit the information
contained in the data. Examples of challenges include the temporal
nature of the data, the variety in the type of data, the different
granularity of various sensors, noise, etcetera.In this course specific techniques to handle quantified self (or broader
sensory data) will be treated. More in specific, it will address:
• Feature engineering (how do we come from raw data to usable features):
* Removing noise from data
* Handling missing data
* Identifying (temporal) features
• Learning of user patterns:
* Temporal machine learning approaches such as recurrent neural
networks, time series analysis
* Clustering approaches with dedicated distance metrics (including
dynamic time warping)
• Adaptive feedback and support
* Reinforcement learning
• Integration of the various components.In addition, a number of real-life applications will be discussed. Next
to lectures, there will be an extensive practical part, where students
will learn to work with various algorithms and data sets. As a final
assignment, the students will work on a project they propose themselves. -
Machine Learning Theory
Course - MasterThis course is part of the Mastermath programme. All information can be found at the Mastermath website.
Machine learning is one of the fastest growing areas of science, with far-reaching applications. In this course we focus on the fundamental ideas, theoretical frameworks, and rich array of mathematical tools and techniques that power machine learning. The course covers the core paradigms and results in machine learning theory with a mix of probability and statistics, combinatorics, information theory, optimization and game theory.
During the course you will learn to
- Formalize learning problems in statistical and game-theoretic settings.
- Examine the statistical complexity of learning problems using the core notions of complexity.
- Analyze the statistical efficiency of learning algorithms.
- Master the design of learning strategies using proper regularization.
This course strongly focuses on theory. (Good applied master level courses on machine learning are widely available, for example here, here and here). We will cover statistical learning theory including PAC learning, VC dimension, Rademacher complexity and Boosting, as well as online learning including prediction with expert advice, online convex optimisation and bandits.
Mathematics
Stochastics and Financial Mathematics
Artificial Intelligence
Logic
UvA -
Managing Digital Innovation
Minor - Bachelor (University)The opportunities of the digital era are essentially unlimited. Innovative technologies may completely change how business and design processes are set up, while new directions for fruitful start-ups are countless. This calls for new and strategic ways of organising these opportunities to innovate in the digital world. If you are interested in new, exciting ways to organise for digital innovation, if you want to learn how new digital technologies such as big data, 3D printing and robotization change the way of working in your own field of expertise; if you are interested in how to design and organise pervasive digital technologies, if you would like to start your own Spotify, Uber or Airbnb in your own specific discipline and would like to learn how to do so; if you are interested in new professional, organisational and managerial insights related to digital innovation, this minor is for you.
Computer Science
Computational Science
Digital Humanities
VU -
Master’s Thesis Data Science and Business Analytics
Course - MasterThe aim of the Master’s thesis is to write an academic paper in which a research question is developed and analysed through original empirical and/or theoretical research, appropriately embedded in the current state of knowledge. Students will be able to synthesise various theories and develop new ones appropriate for a MSc-level student. The Master’s thesis must be written about a subject which is closely related to the field of the chosen specialisation. As a guideline a Master’s thesis should contain 25 to 35 pages, excluding tables and appendices. There are hardly any examples with less than 25 pages and although there are many examples of theses with more than 40 pages, they often include irrelevant material or fail to be sufficiently concise.
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Masters Project Business Analytics
Course - MasterThe Master Project Business Analytics is the graduation project for the Master Business Analytics or the Dual Master Business Analytics. As the graduation project this course builds on and integrates previous courses on Business Analytics. The student shows that he is capable of independent research at an academic level (typically applied in a practical context) on a specific topic in the field of Business Analytics under supervision of one of the staff members of the Faculty of Science. Furthermore, the student writes an academic report to
present the applied scientific methods and the obtained results. Also, the student gives an oral presentation that is tailored to the audience. As such, the skills described below should be demonstrated during the Master Project Business Analytics. -
Master’s Internship Behavioural Data Science
Course - MasterAfter the internship, the student can describe data science activities outside academia (paraphrasing), describe and analyse the client’s data-analytic question (paraphrasing and analysing), work out concrete advice to data science questions (evaluating and scientific thinking), can present this advice for different types of audiences (written and oral communication). Furthermore, the student can incorporate pragmatic considerations (evaluating), assess the importance of methodological considerations (evaluation), reflect on his or her own professional behaviour and adopt a professional attitude when in contact with colleagues and clients (self-reflection and communication).
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Master’s Thesis Behavioural Data Science
Course - MasterAfter completing the MT, the student is able to (a) formulate a methodological research question, or work out a given research question (scientific thinking); (b) delve into the scientific literature in order to get an overview of subject (paraphrasing, analysing and evaluating); (c) design, assess and evaluate a research proposal (scientific thinking); (d) write and present a research proposal (scientific thinking and written and oral communication); (e) collect and analyse data (analysing, evaluating and scientific thinking); (f) write a scientific report (written communication); (g) give a scientific presentation (oral communication).
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Mathematical Optimization
Course - Bachelor (University)Mathematical Optimization is used to take decisions based on quantitative arguments. For most trucks on the road, origin, destination, load and even its route have been determined by an optimization algorithm. The battery life of your phone would be significantly shorter if the chip lay-out was not optimized. Side-effects of radiotherapy would be more severe if cancer treatment was not personalized with state-of-the-art optimization algorithms.
This course will make you familiar with translating practical problems in optimization models, and with solving those models. The focus on practice, rather than algorithms, will allow you to succesfully solve the optimization problems you’ll encounter in your future.
The course covers linear optimization as well as its generalizations (conic and convex optimization). We will briefly consider optimization under uncertainty. Optimization models will be solved with free software.
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Mathematical Statistics
Course - MasterExamples of typical statistical problems are the prediction of an
important quantity on the basis of data, or the question whether some
observed relation between variables is “statistically significant”. In
mathematical statistics we view observed data as realisations of random
variables. A statistical model is a collection of probability
distributions that we interpret as possible distributions of the
observations. A statistical procedure makes a statement about the
question which distribution in the model generated the data. This
perspective makes it possible to cast statistical problems likes the one
mentioned in a general mathematical framework.In this course we introduce this mathematical perspective on statistics
and we treat various statistical procedures (parameter estimation,
testing of hypotheses, construction of confidence sets). Classical
examples are treated and mathematical theory is developed that allows to
assess and compare the performance of statistical procedures. -
Mathematics
Programme - Bachelor (University)By following the Mathematics bachelor programme at the VU, you will learn the fundamentals and discover the unexpected ways in which it can be applied in our society. Mathematics is everywhere: the Google search algorithm uses algebra; credit cards security is done using prime numbers; we can predict an epidemic using differential equations; and Einstein’s theory of relativity is described by using modern geometry. As a mathematician you come up with clear solutions for complex issues.
This bachelor is unique in the Netherlands, because of its strong connection between theoretical and applied mathematics. This is because both the new mathematical theories we develop and the advanced applications of these theories require a thorough knowledge of fundamental mathematics. We will provide you with a strong foundation and you will encounter many practical applications. This course is in Dutch.
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Mathematics
Programme - Bachelor (University)As a prospective Bachelor’s student in Mathematics, you will naturally have an above-average interest in and talent for mathematics. During the first year, you will receive a broad introduction to algebra, analysis, probability and statistics. In the second and third years, you will have the option to focus more on your specific field of interest. Career prospects for graduates in Mathematics are very favourable. Mathematicians who are able to translate complex processes into smart formulas are in demand in nearly every sector, in positions ranging from research analyst at a large multinational to researcher at a governmental organisation.
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Mathematics
Programme - MasterMathematics is a vibrant and versatile field, whereby the dividing line between theory and practice is often merely an illusion. By definition infinite, mathematics finds its way into unexpected applications, with new paths always waiting to be explored. This Master’s programme reflects the versatility of mathematics: it gives you the opportunity to specialise in a particular mathematical area of your interest, while expanding your knowledge of the whole discipline in general. It focuses on current research topics such as: quantum groups; moduli spaces; function theory of several complex variables; applied nonlinear analysis. The programme is offered in full collaboration with the Vrije Universiteit Amsterdam. This lets you benefit from the expertise, networks and partnership projects of the UvA as well as the VU Amsterdam.
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Mathematics
Programme - MasterMathematics is a vibrant, multifaceted and versatile field, in which the focus is on the study and development of techniques to tackle pure and applied mathematical questions. Often, the dividing line between theory and practice is merely an illusion. Mathematical theory, developed for a specific problem, often finds its way into unexpected applications. This is the strength and beauty of mathematics, a discipline which is by definition infinite, and where new paths are always waiting to be explored. The Master’s programme in Mathematics at VU Amsterdam provides you with an opportunity to specialize in one area while further deepening your mathematical knowledge in general. The collaboration with the University of Amsterdam on the entire Master’s programme and with all Dutch universities in MasterMath allows students to choose from a long and varied list of courses.
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MATLAB Applied to Neuronal Data
Course - MasterResearch in contemporary neuroscience requires a solid foundation in data analysis. Data analysis in Neuroscience heavily relies on the ability to use proper software for analysis, as MATLAB. The purpose of this course is to give the students an overview of the advanced analytical techniques currently used in cognitive neuroscience, to provide computational programming skills to implement these analytical techniques using the computational software MATLAB and to use these algorithms to analyze real neuroscientific data.
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MBA Big Data & Business Analytics
Programme - MBAThis MBA in Big Data & Business Analytics is intended for hands-on Big Data specialists, for people in leadership roles working with Big Data and for Entrepreneurs. The curriculum of this MBA is highly multidisciplinary, with courses from A (analytics), B (business) and C (computer science), and with projects to practice and implement the integration of these three aspects.
Furthermore, the curriculum is a mix of state-of-the art theory taught by renowned academic professors, and it includes practical application of this knowledge taught by people with extensive industry experience. In the curriculum, much time will be devoted to the ’21 st century skills’ – the skills required to become successful in this age: entrepreneurship / entrepreneurial attitude, flexibility, teamwork, communication skills and ethics.
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Media and Information: Living Information
Course - Bachelor (University)You live in media – and information is alive. Each of us is the star of his or her own 24/7 reality show. We digitally record, store, edit, and forward almost every aspect of our lives and of the lives of the people around us – whether we want to or not, whether we are aware of it, or not. We produce as much as consume information. Media and information are not just pervasive and ubiquitous – they have become crucial for our survival. This course provides a broad review of all the key definitions, themes and concepts regarding the role media and information play in everyday life.
We will trace the development, examine the content, and explore the impact of media and information on industry and society, reviewing both conceptual and practical aspects of the relationships between new communication technology, media industries, and the issues we are all facing in everyday life: understanding and managing careers, relationships, and identities.
Information Cultures (Media and Information)
New Media & Digital Culture (Media and Information)
Archival and Information Studies
Media en cultuur
Media and Culture
UvA -
Medical Informatics
Programme - Bachelor (University)Successful information dissemination in medicine requires people with an understanding of both medicine and information science. The Medical Informatics programme offers an exciting mix of programming, mathematics and medical courses. Practical work is an important element of the programme. The Bachelor’s programme in Medical Informatics at the University of Amsterdam is unique within the Netherlands and stands out from other informatics study programmes by situating itself within a medical context. You will attend lectures on the anatomy and workings of the human body, and on the causes and categorisation of illnesses. You will also gain an insight into how doctors use reasoning, and learn to use equipment, information systems, and programmes and methods for analysing and presenting medical data. Financial, ethical, legal, business and economic aspects of healthcare are also considered. You will also spend time on software engineering and project management. This is a small-scale programme with a friendly, informal atmosphere.
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Medical Informatics
Programme - MasterA Medical Information specialist is familiar with all the basic medical subjects, the way in which a doctor reasons and acts, the methodology of medical-scientific research and the organisation of healthcare. The Medical Information specialist distinguishes himself from other information specialists and information scientists through his knowledge of medical processes, care organisation processes and his insight into the specific role and meaning of information in the healthcare sector. The medical information specialist is an expert in the field of information analysis, information representation, system design, and implementation and evaluation of information systems, and in lesser extent in the field of the development of advanced technologies on which information systems are based. A medical information specialist is a skilled consultative partner of information and communication technologists as well as doctors and nurses, and thus acts as an essential bridge between the two divergent fields of medicine and informatics.
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Medical Natural Sciences
Programme - Bachelor (University)The healthcare sector is developing at a rapid pace. Recent developments are the use of smartphones as a tool for diagnosis, remote-controlled pacemakers and 3D-printed organs. During the bachelor Medical Natural Sciences you will learn about innovation in healthcare. You will study physics, chemistry, mathematics, informatics and physiology in a medical context. As a result you will be able to look beyond your own area of expertise, and solve complex medical issues.
The Medical Natural Sciences bachelor programme at the VU is unique in the Netherlands. Out of all the programmes at the VU related to healthcare, this is the most exact. Medical Natural Sciences therefore is a collaboration between the VU beta faculties and the VU university medical centre: VUmc. Both are practically housed in one place at the VU campus in Amsterdam. After graduating from this programme, you will know how complex machines in hospitals work and how you can improve them. As a medical physicist you will become the doctor of the future. This course is in Dutch.
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Mentoring Startups
Course - Professional educationOur Mentoring Start-ups Masterclass aims to further develop your mentoring and coaching skills. During 2 half-day sessions on 15 April and 24 June 2020, you get updated on the scientific basis and best practices of startup coaching and mentoring. There are 2 months between sessions, so you have the opportunity to put into practice and reflect on what you have learned. In between these two meetings at the University of Amsterdam Business School, two informal discussion sessions will be organized in collaboration with start-up accelerator Rockstart based in Amsterdam about start-up mentoring on 29 April and 27 May 2020. These sessions will also be open to Rockstart’s mentors to facilitate the informal exchange of experiences between startup mentors.
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Methods of Communication Research and Statistics
Course - Bachelor (University)In Methods of Communication Research and Statistics students acquire knowledge and insight into research designs, methods of data collection, and data analysis including descriptive statistics and inferential statistics. The course consists of lectures (on Monday and Tuesday) and tutorials (on Monday and Tuesday). In the lectures the theory is introduced and statistical techniques are explained, while in the tutorials the literature is examined more deeply and practical skills, including academic skills, are addressed (discussing, practicing and writing about important decisions during developing a research design, calculating and interpreting statistics and working with the statistical software package SPSS). Methods of Communication Research and Statistics will be assessed through two-weekly multiple-choice tests, five group assignments, and two exams. Students prepare for the lecturers by reading assigned literature and watching micro lectures. Students prepare for tutorials by completing specific homework assignments.
Minor Communication Science (HBO Follow-on minor)
Bachelor’s Communication Science
Minor Communication Science 60 EC
Bachelor’s Communication Science (Short-track)
UvA -
Microeconometrics
Course - MasterIn the microeconometrics course about ten recent empirical papers that apply micro-econometric estimation techniques are considered. These empirical papers usually concern issues like individual choice behaviour in the labour and consumer markets. The focus will be on nonlinear techniques applied to discrete, censored, truncated dependent, count, duration etc. variables. Apart from these papers, microeconometric theory from the book by Cameron and Trivedi has to be studied in order to understand the papers. During the computer classes the techniques will be applied. The statistical software MatLab will be used to estimate likelihood functions, etc.
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Minor: Bioinformatics and Systems Biology
Track - MasterResearch in Bioinformatics in its broadest definition concerns the analysis of informational processes within living systems with the help of computers. To do this succesfully, Bioinformatics actively uses and integrates contributions from areas such as Mathematics, Computer Science, Chemistry, Medicine and Biology. Bioinformatics has recently become one of the keywords in the life sciences as well as in Biotechnological and Pharmaceutical industries. Although in essence the field exists for over two decades and bioinformatics techniques developed over the years have come of age, the field has gained major prominence relatively recently, owing mostly to the world-wide human genome projects and subsequent structural and functional genomics initiatives.
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Modelling and Simulation
Course - Bachelor (University)After following this course you will be able to: Formulate suitable models for a range of problems and explain your choices; analyse and solve simple models analytically; implement simple mathematical models in code and verify and validate the correctness of your implementation; explain and analyse how discretisation and the application of numerical approximations affect the outcome of your simulations; explain the power and the limitations of models; explain the concept of model fitting and describe some common techniques; describe relevant properties of several classes of models and explain their meaning.
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Multivariate Econometrics
Course - Bachelor (University)This course covers both theoretical and practical aspects of modeling
multivariate non-stationary time-series and panel data, with special
emphasis on unit-root processes and cointegration.The students will be introduced to linear multivariate time-series
models and linear panel data models used in econometrics. Important
topics include marginalizing, conditioning, exogeneity, vector
autoregressive (VAR) models, and vector error correction models (VECM).Important limit results will be carefully derived providing the students
with a deep understanding of the theory and practice behind a wide range
of advanced unit roots test, spurious regression, cointegration, and
dynamic panels. -
Multivariate Statistics
Course - MasterThis course introduces the theory and applications for analyzing
multi-dimensional data. Topics include multivariate distributions,
transformation of variables, Gaussian models, fat-tailed multivariate
distributions, copulas, mixture models, multivariate inference and the
Delta method, dimension reduction methods such as principal components
and factor models, and clustering methods. -
Multivariate Statistics & Machine Learning
Course - Bachelor (University)Most psychological research involves multivariate observations. This can be in the form of repeated measures or multiple dependent and independent variables. A score of multivariate procedures belong to behavioural scientist’s toolbox. These include the General Linear Model (GLM, including MAN(C)OVA and Repeated Measures analysis), Principal Components, Linear Discriminant Analysis, Logistic regression, and clustering methods. Few people realise that these exact same methods, many of which were specifically developed for research in the behavioural sciences, also power today’s large scale Machine Learning technology.
The course covers the most commonly used multivariate methods. We will discuss and practice their use for statistical inference and their use in Machine Learning. Specifically, we discusss multiple and multivariate regression, model selection and cross-validation, multivariate analysis of (co-)variance (MAN(C)OVA), linear discriminant analysis and its relation to MANOVA, multiway ANOVA, Principal Components Analysis and Principal Axis Factoring, and Multiple Logistic Regression. We will discuss these at the level of the underlying matrix algebra, the use of existing software, diagnostics, and the verifications of underlying assumptions when used for statistical inference. We use these methods in applied machine learning projects. Crucial differences between statistical inference and machine learning applications are highlighted.
Bachelor’s Psychology
Exchange programme Exchange Programme Social and Behavioural Sciences
Minor Psychology Behavioural Data Science (for ISW students)
UvA -
Natural Language Processing 1
Course - MasterThis course aims at providing the student with the background that is needed for studying statistical models that are used in the field of Computational Linguistics. We will mostly depart from shallow labeling tasks and consider tasks that involve hierarchical structure (e.g., syntactic trees) and/or hidden structure (alignment of word and their translations in machine translation). For these tasks the course will concentrate on the fundamentals of probabilistic modeling and statistical learning from data by supervised and unsupervised statistical learning algorithms.
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Natural Language Processing 2
The amount of language data that is available to us electronically is increasing with the day. With this eminent increase, a question arises as to the possibility of inducing latent structure in this data that can be useful for further tasks such as machine translation. The different kinds of latent structure that is possible depends on the data and the task, and will usually demand suitable statistical models and learners. The course will teach methods for inducing a variety of latent structure for tasks such as language modeling, machine translation and adaptation across domains. The course covers the following topics
- Machine Translation and Paraphrasing
- Domain adaptation
- Latent, hierarchical or linguistic structure in natural language data
The course will further dive into a selection of advanced and current NLP topics, such as contextualized embeddings, deep generative models, speech recognition, neural language modelling, interpretability.
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Nonparametric Statistics
Course - MasterThis course is part of the Mastermath programme. All information can be found at the Mastermath website.
Master’s Mathematics
Master’s Stochastics and Financial Mathematics
UvA -
Object-Oriented and Functional Programming
Course - Bachelor (University)The goal of this course is to obtain familiarity and experience with
advanced programming language concepts, such as inheritance and pattern
matching, as well as improving general programming skills.After taking this course, you will be able to:
* Understand & apply concepts from object-oriented programming such as
subtyping and inheritance.
* Understand & apply concepts from functional programming such as
pattern matching and higher-order functions.
* Design and implement a moderately large program from scratch.
* Produce clear, readable code. -
Offensive Technologies
Course - MasterPlease refer to the System and Network Engineering web pages for detailed and current course information.
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Operations & Supply Chain Management
Course - Professional educationEen van de relevante ontwikkelingen voor CFO’s is een verdere uitbreiding van het takenpakket van controller naar CFO, waarmee de verantwoordelijkheid van de controller wordt verruimd. Hij/zij wordt een prominent lid van het Management Team, waarbij een veel bredere kijk op de processen binnen de organisatie noodzakelijk wordt. Deze module over operations en supply chain management sluit hierop aan.
Om de hoofdstructuur van de module te bepalen, is gebruikgemaakt van een integraal operations management concept. Van een integraal concept is sprake als er op een samenhangende wijze beslissingen worden genomen over: de primaire processen (samen met partners in de keten), het plannings- en besturingsmodel, de ondersteunende ICT en de operations management organisatie.
De leerdoelen van het vak zijn:
- kunnen beschrijven hoe strategische bedrijfsdoelen moeten worden vertaald naar operations management doelstellingen;
- een kritisch oordeel kunnen vormen over de bestaande operations management op basis van wetenschappelijk onderbouwde modellen over operations management en supply chain management door de sterktes en zwaktes te kunnen benoemen;
- de rol van externe partners (klanten, toeleveranciers en dienstverleners) kunnen beschrijven op basis van een gekozen operations management strategie;
- een integraal aanpak voor operations management kunnen beschrijven voor een onderneming op basis van de elementen grondvorm, planning en besturing, ICT en organisatie;
- voorstellen voor verbeteringen in operations management kunnen onderbouwen met een kosten/ baten analyse en een risico-analyse;
- de rol van controllers in operations management en supply chain management.
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Operations Performance Benchmarking
Course - Bachelor (University)Performance assessment and benchmarking is a topic that has received
considerable attention in both practice and academia across a wide
variety of disciplines. This course is aimed at students who wish to
broaden their understanding of methods related to evaluating and
benchmarking performance in operations. The course will focus on
academic methods relevant to benchmarking of operations performance in
business practice.The course introduces an array of quantitative approaches for
performance benchmarking, making use of various statistical and
mathematical optimisation techniques. The course teaches how to define
and compute performance indicators and how to interpret these results
properly in a business context.The course is self-contained, it does not rely on other TSCM courses.
It is therefore also accessible to students without prior knowledge of
TSCM. -
Operations Research
Course - Bachelor (University)The course is a first introduction to optimization problems. We start with linear optimization. Many practical problems allow mathematical formulation as optimization of some linear objective function in decision variables subject to a set of linear constraints in these decision variables. A central theme will be the art of formulating a verbally described practical problem as a linear optimization problem and interpreting the mathematical solution within the original problem. The simplex algorithm for solving the mathematical model will be studied and correctness of this algorithm will be argued.
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Operations Research I
Course - Bachelor (University)This is an introductory course in deterministic optimization. The
optimization models studied are unconstrained non-linear optimization,
constrained non-linear optimization, convex optimization, linear
optimization and integer linear optimization. Solution techniques for
these classes of optimization problems are the central theme of this
course. Another important element of the course is the mathematical
formulation of (practical) verbally described problems as instances of
the optimization models, and application of the solution methods to
solve the resulting problems. -
Operations Research II
Course - Bachelor (University)This is an introductory course in stochastic models. It builds upon the
basic course in probability theory and extends the theory of static
probability to dynamic stochastic processes. The course focuses on
Poisson process, discrete-time and continuous-time Markov chains, with
applications to queueing models, network models, risk analysis,
reliability problems, etc It also discusses dynamic optimization and
stochastic simulation of these systems. -
Operations Research III
Course - Bachelor (University)A student who successfully completes the course will have an
understanding of the techniques of combinatorial optimization and
integer programming, and be ready to apply them to problems encountered
in practice.* The notion of efficiency in algorithms; distinguishing between
tractable and computationally “hard” problems.
* The correctness and efficiency of key algorithms in combinatorial
optimization will be shown rigorously. Problems studied will include:
minimum spanning tree, maximum flow, minimum cost flow, and matching.
* Formulation of problems as integer programs; the notion of the
strength of a formulation; the central role of integral formulations.
* The main techniques and theory used in commercial integer programming
solvers such as Gurobi will be investigated in detail. A main focus will
be on the powerful cutting-plane method.
* Column generation, Lagrangian relaxation, modelling of
disjunctions,and other problem-tailored techniques will be discussed.
* Experience in the use of integer programming solvers will be gained.
* Basic knowledge of Python will be gained. -
Optimization
Course - Professional educationTo develop skills to formulate mathematic models for practical problems as linear programming (LP) problem or dynamic programming (DP) problem. To learn the general techniques for solving deterministic Operations Research problems. To formulate and solve LP-problems in Excel
Actuarial Science
Econometrics
Actuarial Science (minor)
UvA -
Optimization of Business Processes
Course - MasterWe deal with a number of application areas of stochastic modeling: production logistics, call centers, health care and revenue management. For each area we present quantitative problems and discuss how they can be solved using mathematical models. We also discuss a number of new models. Several guest lectures are given by people from industry.
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Parallel and Distributed Computer Systems
Course - MasterIf you want to reach the top of the field of experimental computer science, PDCS is your program. Our Top Master’s program in Parallel and Distributed Computer Systems was founded by prof. Andrew S. Tanenbaum and is designed to challenge students with the hardest problems in modern systems-oriented computer science. The program aims at highly talented students and is selective, focusing on excellence. After finishing this master, many students move on to pursue careers at leading companies like Google or Microsoft, PhD programs in top research schools, or join R&D labs in the industry.
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Parallel and Distributed Computer Systems
Programme - MasterAs the internet develops, it has come to include cloud computing centres, smartphones, RFID tags and sensor networks. This connected world brings new opportunities to science and business, but also new challenges to privacy and security. You will study entirely new software architectures and large-scale, geographically distributed systems which can serve billions of users. Scalability, performance, security and visualization are key topics.
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Parallel Computing Systems
Track - MasterParallel computing systems are ubiquitous today. From laptops and mobile phones to global-scale compute infrastructures, parallel computing systems drive the world we live in. Although motivated by advances in hardware design, the many-core revolution has a profound impact on engineering software: Only software explicitly dedicated to parallel architectures can fully exploit today’s hardware potential and benefit from future gains in hardware performance. Only software engineers who are true experts in parallel computing systems can make an impact on future software.
For this track, leading research groups in the areas of parallel system architecture, programming parallel systems, and performance optimization team up to educate the future experts of the many-core age. This track covers all aspects of parallel computing systems, from hardware to software, and the entire range of scale from laptops to compute servers, GPU accelerators, heterogeneous systems and large-scale, high-performance compute infrastructures. The track includes much practical work that uses a unique, world-class infrastructure, the Distributed ASCI Supercomputer (DAS). Being around for almost two decades, the brand new 5th generation system DAS-5 covers the entire range of scale of parallel systems today and is equipped with a variety of the latest many-core devices. The track also optimally benefits from the local SURFsara supercomputing center and the Netherlands eScience Center, that both are involved in numerous real-world applications.
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Parallel Programming Practical
Course - MasterAt the end of the course, the students will:
– be able to apply three important paradigms in parallel programming:
distributed message passing, master-workers, and
Partitioned Global Address Space
– have practical experience with a real programming system for each of
the three paradigms: MPI, Java/Ibis, resp.
Chapel
– be able to benchmark and analyse the performance of parallel programs
on a real machine (a cluster computer) and to
write a short scientific report about this. -
PDCS Programming Project
Course - MasterPDCS programming projects are related to existing research programs in
computer systems. There is no set course description as each project is
negotiated individually with the permanent staff member supervising and
grading it.The assignment aims to offer students a challenging project
that is research-oriented by nature. Students are supposed to talk to
staff members individually to see whether they have a project that
matches the student’s interests.Next to the computer program, a written report must be produced in which
the idea behind the program is described, as well as the novelty and how
it fits in the context of the overall research project. The student
should
also describe lessons learned, and reflect how the project builds on
knowledge acquired in earlier Bachelor and especially Master courses.The final mark is based on the quality of the programming work (50%),
the written report (30%), and the academic excellence shown and effort
invested by the student during the project (20%). -
Predictive Modeling
Course - Professional educationIn many decision issues, there is a desire to know the future events, so that the best decisions can be taken. Based on historical data, it is possible to extract patterns that say something about the future. The process to fit data to a mathematical model, to make the best possible forecast, is called predictive modeling. This module provides an overview of the most relevant techniques and we do this by applying them on datasets.
Please note that this course cannot be followed separately.
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Principles of Bioinformatics
Course - Bachelor (University)Are you interested in bioinformatics? Would you like to know how huge amounts of data can be analysed in order to discover new biology? Would you like to solve open questions in scientific research? This course is open for any Bachelor student in a Science Degree (including Biology or Biochemistry). Principles of Bioinformatics is the starting course for bioinformatics at an Academic level. It aims to give a broad overview of important topics relevant to the field, with a focus on current (open) problems in bioinformatics research. During the lectures and practical sessions you will become familiar with practical solutions, but also discover that there is still a lot of room for improvement in this rapidly advancing field of research.
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Privacy in Public: Big Data, Self Tracking, Social Networks
Course - Bachelor (University)The second of two introductory courses of the Minor Privacy Studies offers interdisciplinary education on contemporary privacy developments and issues. Students will dive into the world of Big Data, Self-Tracking and Social Networks. The course trains students to engage with the social, legal, ethical, and economic challenges posed by the exploding use of information technology. The course builds on the knowledge that was gained in the first introductory course. Students will employ the discipinary knowledge that they have gained as well as their understanding of how different disciplines need to work together, and how they can profit from each others’ research. In the seventh week, a hands on privacy workshop will be organized, for which students are asked to prepare.
Students that have not taken the first course are not excluded from participation. However, the two courses are purposefully connected and students that complete them consecutively will be better equipped for the complex field of Privacy Studies.
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Privacy: Theoretical Perspectives, Future Challenges
Course - Bachelor (University)In this first of two introductory courses of the Minor Privacy Studies, students will be introduced to central privacy theories and challenging current dilemmas within six central disciplines. The lectures will be given by top scientists from various faculties of the University of Amsterdam and other universities. In this multi-disciplinary introduction, students are provided with detailed knowledge of the principles and values of privacy. They will be prepared to engage in a true interdisciplinary fashion later on by an interactive debate on central privacy values that were identified in the course, inspired by actualities in the field and society as a whole.
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Probability and Statistics
Course - Bachelor (University)Many phenomena are subject to chance variation: economic time series, sampling of respondents in a survey (and subsequent lack of response), measurement error, survival after a medical treatment, physics of large systems, etc. Probability theory is the mathematical formalism to model such diverse phenomena. This course starts by introducing key concepts of probability theory: random variables and vectors, probability distributions and densities, independence and conditional probability, expectations, law of large numbers and central limit theorem.
Probability models are the basis for statistical analysis. Whereas descriptive statistics is concerned with averages and numerical tables, statistical inference tries to answer scientific questions regarding financial series, earthquakes, the health effects of certain foods, etc. This is done by modeling data as the outcome of a chance experiment. Statistics next aims at inferring the probability model for this experiment from the data. Methods are developed, understood and investigated from this perspective. Drawing up a reliable model for the underlying chance experiment is not always easy, but once available this allows making optimal decisions and quantifying the remaining uncertainty, and opens up possibilities for generalization. Key concepts discussed in this course are likelihood, estimation, testing, p-value, confidence regions, risk and power functions, Bayesian inference. The emphasis is on concepts, but well-known concrete methods as the t-test arise as
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Probability Theory
Course - Bachelor (University)We study experiments in which randomness plays a role. We first consider discrete probability experiments, that is experiments with a countable number of possible outcomes. You can think of tossing dice, shuffling a deck of cards, flipping coins etc. The possible outcomes form a set, the so called sample space. Every subset of this sample space is an event. We assign probabilities to events in a reasonable way, such that the three axioms of probability are satisfied. We compute probabilities in these situations and consider associated concepts like independence, conditional probabilities, random variables and important discrete probability distributions like the Bernoulli, Binomial, geometric, hypergeometric, negative Binomial and Poisson distribution.
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Probability Theory and Statistics 1
Course - Bachelor (University)During the lectures all of the below mentioned topics will be discussed step-by-step, starting with the axioms of probability theory. Proofs of theorems will be provided and examples will be discussed. The students prepare for each tutorial by solving a number of exercises. During these tutorials the exercises will be discussed by the teacher and will serve as an illustration of applications of the theory. In these classes there will be ample time for raising questions and discussion. Sometimes, a part of the tutorial will be used to discuss an important theory.
Topics:
- descriptive statistics (measuring scales, mode, median, average, variance, percentile, graphical techniques);
- population and random sampling;
- experiments, outcome space and events;
- definitions of probability and axioms of probability theory;
- conditional probability, independence and Bayes’ Theorem;
- combinatorics, counting techniques, permutations, combinations and binomial theorem;
- sampling with or without replacement;
- discrete versus continuous random variables, pdf and CDF;
- expectation, variance and (higher) moments;
- inequalities of Markov, Chebychev and Jensen;
- moment generating functions;
- specific discrete distributions and their properties: uniform, binomial, hypergeometric, geometric, negative-binomial and Poisson distribution; Poisson-processes;
- specific continuous distributions: uniform, exponential, gamma, chi-squared and normal distribution, as well as approximations with the latter one of discrete distributions;
- univariate transformations, pdf-, CDF- and mgf-methods;
- inverse CDF method for the generation of random variables from a desired distribution;
- location and scale parameters.
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Probability Theory and Statistics 2
Course - Bachelor (University)After creating a sound theoretical basis, the theory will be extended step-by-step in the course, proofs of the theorems will be given and examples will be discussed extensively. The exercises included in the reader are an essential part of the course to get acquainted with the presented theory. The students prepare for each tutorial by solving a number of exercises. During these tutorials the exercises will be discussed by the teacher and will serve as an illustration of applications of the theory. There will also be time for raising questions and discussion.
Topics:
- multivariate distributions, joint pdf and CDF and marginal distributions;
- independence of random variables, covariance and correlation;
- conditional distributions, expectations and variances;
- variance-covariance matrix;
- simultaneous moment generation functions;
- multinomial and bivariate normal distribution;
- multivariate transformations: CDF, transformation, and mgf method;
- distributions of sums of random variables and convolution formula;
- T and F distribution;
- random samples: empirical CDF, sample statistics, distribution of sample statistics for samples from normal and binomial distributions.
Application to order statistics; - point estimators, confidence intervals and hypothesis testing for population mean, population variance and population proportion as well as for differences of those parameters between two populations;
- power and p-value.
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Probability Theory and Statistics 3
Course - Bachelor (University)The student obtains fundamental insights into classical mathematical statistics and is able to work with basic statistical models. Students will be able to:
- demonstrate and apply various statistical convergence notions, including convergence in distribution and convergence in probabilty;
- convergence in probability, stochastic convergence and asymptotic normality;
- evaluate and compare point estimators based on properties of these estimators (consistency, efficiency, relative efficiency);
- apply various methods for finding a ‘uniform minimum variance unbiased estimator’ (UMVUE) of a given parameter;
- work with the concepts ‘completeness’ and ‘sufficiency’ and understand the role of these concepts in deriving optimal estimators, confidence intervals and statistical hypothesis tests;
- use various techniques to derive confidence intervals and statistical tests;
- understand and derive elementary properties of tests such as the size and power of tests;
- command several ways to find uniform most powerful tests;
- derive and apply generalised likelihood ratio tests.
Minor Actuarial Science
Bachelor’s Actuarial Science
Minor Actuarial Science
Bachelor’s Econometrics
UvA -
Process Analytics & Semantic Web
Course - Professional educationIn this module we look at how we can use the data present in an organization to improve business processes. This involves analyzing events (event data) through process mining. We also look at how existing data can be ‘enriched’ (in line with the so-called semantic web, using ontologies). In this way data can be made interpretable for computers and we can also reason with this.
Please note that this course cannot be followed separately.
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Programming and Curating Audiovisual Media
Course - MasterAs the production of audiovisual media has dramatically increased in recent years, while more archival material has been made available, what exactly is being shown, and when, where, how, by whom and why? Different definitions of programming and curating will be considered. How do programmers and curators select and contextualize material, and participate in processes of knowledge production and value creation? How can we understand underlying issues of power, authority, and ethics? Attention will be paid to historical and current practices of presenting film, television and media art, from the classic cinematic dispositif to digital transformations. How are institutional policies regarding validation of audiovisual heritage attuned to such developments? We will distinguish between different scales of presentation, from artists’ initiatives to GLAM manifestations, and from individual viewings to the film festival circuit. How do these practices set agendas, contribute to media theory and historiography, and help to understand the place of moving images in society? Students will learn key concepts and specific demands of presenting audiovisual media, and to combine theory with practical activities. Students will also be asked to apply their (media) historical knowledge to present audiovisual material in context.
Dual Master’s Preservation and Presentation of the Moving Image (Media Studies)
UvA -
Programming and Numerical Analysis
Course - Bachelor (University)This course has as objective to provide the student with a solid basis of computer programming as indispensable skill for the remaining study in econometrics, operational research and actuarial science. After (successful) completion of this course students are able:
– to formulate algorithms themselves for simple mathematical problems -such as the Euclidean algorithm to calculate the greatest common divisor or optimization algorithms- especially during computer class;
– to create a computer program in order to solve simple problems/tasks such as: approximating roots of non-linear functions, optimize a non-linear function, calculate eigenvalues and vectors and conduct a small simulation study;
– to demonstrate the use of the following fundamental methods for numerical analysis: finding roots of equations, solving systems of linear equations, numerical interpolation and integration, especially in final exam and computer assignments.
Actuarial Science
Econometrics and Operations Research
UvA -
Programming and Numerical Analysis
Course - Bachelor (University)Much attention will be paid to the development of so-called algorithmic thinking, by which the student will be endowed with the skill of structuring and solving mathematical problems in a systematic way. Designing algorithms for (simple) mathematical problems is irrespective of a specific programming language. One’s own algorithms will be implemented in a program so that the algorithm can be executed and verified. Apart from translating an algorithm into a computer language, attention will be paid to a number of topics from numerical analysis. The techniques of numerical methods will be applied in an econometric context.
Specific topics are:
- root finding: bisection, fixed points, Newton-Raphson and secant method;
- interpolation: Lagrange polynomials, splines, polynomials on the basis of OLS;
- numerical integration: trapezium, Simpson’s rule, improper integration
- optimisation: Newton’s method, Steepest ascent.
Bachelors Econometrics
Bachelors Actuarial Science
UvA -
Programming C++
Course - MasterThe course consists of the introduction into the syntax of the C++ programming language and object-oriented programming.
The course is scheduled on five days spread over two weeks. The work load and home exercises are extensive, so the student should be prepared to spend the full two weeks on this course.
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Programming for Astronomy & Astrophysics
Course - MasterThere are three general areas in astronomy where computers play an important role. First, the latest generation of observatories produce high data rates that at times need to be searched in real time for interesting signals. Second, there is a move to more open data sharing and public archiving of observational data, which creates opportunities for data mining. Third the availability of massive amounts of computational power allows for increasingly detailed astrophysical simulations.
The aim of this course is to teach you the set of programming skills that an astronomical researcher needs. It is primarily aimed at beginners and those with relatively little experience in programming and focuses on the basics of the Python programming language and the use of modules for astronomical research in the first half of the course – object oriented programming is not covered in any detail. During the second half of the course, we introduce techniques to properly document and test your code, to analyse and improve the efficiency and speed of your code and teach you how to make your code publicly available via github.
The course is taught mostly via guided learning using online course materials and jupyter notebooks, with a weekly tutorial to provide support and which includes short lectures to introduce the more advanced topics.
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Programming for Brain and Cognitive Sciences
Course - Master -
Programming for Economists
Course - Bachelor (University)In this course, students will learn how to program in the programming
language Python. Rather than focusing on Python-specific topics, the
course focuses mainly on the basic principles of programming, with
emphasis on good programming style and structure. These principles are
applicable to any imperative programming language, which makes it easier
for students to adapt to other programming languages during their
further research or study.After finishing the course, students will have an understanding of the
basic principles and concepts of programming. Students will be able to
read and write (simple) programs and algorithms in Python, and use the
computer to solve problems in a structured manner. -
Programming for MNW
Course - Bachelor (University)Starting from the very basic elements of python, very interesting
computational problems can be solved. The knowledge is built from
specific examples of data handling that are of interest for medicine or
biology. The examples range from simply storing data, to implementing a
mathematical model of the dynamics of a population from which, an
animated image is created, to describing and predicting exponential
growth of a plague after contention measures are applied. At the end of
the course the student will be able to create python scripts to solve
some of the data analysis problems they are expected to find in the
medical field. -
Programming for Psychologists
Course - MasterYou will learn how to design psychological experiments and how to
implement these using the OpenSesame software package and the Python
programming language. You will be mainly working with
OpenSesame, which is specially designed for constructing experiments. To
successfully create experiments in OpenSesame, however, you need a basic
understanding of Python. Therefore this course will also address general
programming principles that will facilitate the learning of other
programming languages in the future. We will furthermore look at how to
efficiently design behavioral experiments, with the focus on
randomization procedures, how to present visual stimuli,
and on how to record responses of participants. -
Programming in Matlab
Course - Bachelor (University)The aim of this course is to learn to program at a level that enables the student to write relatively simple calculation programs. Matlab is used here because this language is low-threshold. The material includes: program structure, modular programming, functions, matrices, file I / O, visualization, debugging, and practicing with physical geographic and biological examples.
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Programming in Psychological Science
Course - MasterProgramming is an important research skill. Being able to program allows one, for instance, to investigate psychological models by simulations, develop and implement new statistical techniques, manipulate and analyse data in various ways, and make computer experiments.
The use of R is rapidly increasing in psychological research. R is easy to learn, free and platform independent (http://cran.r-project.org). In the first two weeks of this course we focus on R, and provide basic knowledge of the most important concepts of programming in R.
In the last two weeks you can choose among different tracks that focus on either more advanced R skills or on other programming languages such as Python, which can be used to create interactive experiments of any complexity (from simple two-button response tasks to game-like environments) and is particularly well-suited to psycho(physio)logical lab experimentation due to easy interfacing with various hardware set-ups.
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Programming in Python for Text Analysis
Course - MasterDuring this course, you will learn how to analyze text data using the
Python programming language. No programming knowledge is required; we
believe that anyone can learn how to program.You will learn how to extract information from text corpora; deal with
different file types (plain text, CSV, JSON). We will focus on
readability and understandability of your code so that you will be able
to share it with others, and reuse your code in the future. -
Programming Large-scale Parallel Systems
Course - MasterThis course discusses how programs can be written that run in parallel on a large number of processors, with the main goal of reducing execution time. The class has a brief introduction into parallel computing systems (architectures). The focus of the class, however, is on programming methods, languages, and applications. Both traditional techniques (like MPI message passing) and more advanced techniques like parallel object-oriented approaches from the Java ecosystem or dedicated HPC programming languages (like Cray’s high productivity language Chapel) will be discussed. Several parallel applications are discussed, including nearest-neighbor stencil computations, N-body simulations and search algorithms.Programming Large-scale Parallel Systems
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Programming Multi-core and Many-core Systems
Course - Bachelor (University)The course provides a comprehensive introduction into state-of-the-art programming models for concurrent computing systems from multi-core processors in everyday laptops to large-scale server systems and high-end accelerators.
We start with instruction-level parallelism and vectorisation. Then we continue with multithreaded programming models for shared address space systems, where we look both into OpenMP compiler directives and into more low-level Posix threads before we discuss advanced topics and common pitfalls of shared memory parallel programming.
Towards the end of the course we focus on general-purpose graphics accelerators (GPGPUs) using NVidia’s programming model CUDA and end with advanced topics such as directive-based GPU programming and programming heterogeneous systems.
The lectures are complemented by labs where participants gain first-hand experience with the various programming models. Participants present their work and discuss their achievements with each other as well as with the lecturers and lab assistants during four bi-weekly workshops (werkcolleges).
The course is complementary to the VU courses Programming Large-scale Parallel Systems and Parallel Programming Project in that it looks into node-level concurrency, whereas the other courses focus on systems that are made up of many nodes.
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Programming your World
Course - Bachelor (University)The main thread of the course, Principles of Programming, will equip the student with a fundamental understanding of how to design programs to solve computational problems. As the title suggests our emphasis will be on the important ideas and principles of computer programming and how to apply them to design solutions to computational problems in your world, i.e. the world of the student, as well as applications in the sciences:
- social networking: friend networks, gossip networks, networks of business contacts, complex networks and their application in tracking the spread of diseases such as influenza and HIV, protein-protein networks.
- web: linking, text processing, parsing, searching, filtering.
- email: text processing, searching, filtering, contacts, contact patterns.
Scientific reasoning will be encouraged through an inquiry-based approach by promoting “whatiffery”, a try-it-out mentality where the student seeks answers to his/her own questions by thinking up test cases to answer what-if questions and then programming and executing those test cases.
In Programming Your World we will deal with two programming languages, one for learning the principles of programming and one to apply them.
- The principles will be taught using the Racket programming language in the DrRacket programming environment. In keeping with our principles-based approach Racket is chosen for its simplicity of syntax and exceptionally clear semantics as well as for the wide variety of programming paradigms to which it lends convenient expression. Using scheme we avoid all unnecessary complexity and promote experimentation using its interactive, incremental development environment.
- Python is a high-level, scripting language with many available libraries that can be “glued” together. Its syntax is clear, readable and expressive.
The course is founded on the principle of open knowledge: All texts are freely available online as well as in book form. The programming environment and all code used are open source and freely downloadable.
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Programming: The Next Step
Course - MasterIn this course students will create their own software program from start to finish over the course of four weeks. Students choose an assignment to program depending on programming language preference and personal interest. The first week handles the software requirements and design. The second and third week is used for actual implementation and testing. The fourth and final weeks focus on testing, improvements, reporting and presentation of the program.
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Project Big Data
Course - Bachelor (University)After completing this course:
1. the student can transform and explore data with the command line
2. the student can extract data with regular expressions
3. the student can import and process static and streaming data in
Python
4. the student can store and retrieve semi-structured data in and from
a database
5. the student can parallelize tasks via MapReduce, threads and/or
queues in Python.
6. the student can create appropriate and well-formatted visualizations
and tables
7. the student can address a research question and report on their
findings -
Project Business Analytics 1
Course - MasterThe objective of the course is to expose students to business analytics in Microsoft Excel: the student should be able to solve (practical) business problems using Excel, to write a management report on it, and to give a clear presentation about it.
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Project Business Analytics 2
Course - Bachelor (University)You will work in teams to solve a simplified business case about risk management using a simulation model in Excel and Crystal Ball. You will use knowledge obtained in other Business Analytics courses in the first year, in particular the courses Probability Theory and Risk Management to build and analyse your model. You will write a report and give an oral presentation on your results and conclusions.
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Project Computational Science
Course - Bachelor (University)In this project you will design, implement and test a simulation program for a computational problem of your own choosing. You will use this program to perform a set of experiments and you will interpret and present the results of your experiments. You will be working under the guidance of an experienced researcher.
1. The project requires analytical skills, both in constructing a simulation model and in analysing the simulation results.2. The project includes some implementation work, but this should not be the dominant component.3. The project includes a fair amount of experimentation.4. The project results in a report and a presentation. The presentation may include a short demo.
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Quantitative Marketing
Course - Professional education -
Requirements Engineering
Course - MasterThe success of a software system depends on the proper interpretation and analysis of user needs. Experience shows that it is extremely difficult to adequately define and specify a system. The perception of customers and users of the problem is often incomplete, inaccurate and changes over time. Knowledge is hard to express and to transfer. During this course you will understand why user needs are so hard to express, capture and understand. You will also learn the shortcomings of best practices like scrum, prototyping, interviewing and use cases. Furthermore you will learn about data-driven methods for requirements engineering like Contextual Design.
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Risk Management for Financial Institutions
Track - MasterYou learn to price derivatives, such as share options, and strategies for risk management. You will deal with both practical and theoretical aspects of the discipline.
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Robot Law and Artificial Intelligence
Course - Bachelor (University)Robots and Artificial Intelligence used to belong to science fiction
movies and stories. Also, they were discussed in theoretical academic
and popular articles. In recent years both Robots and Artificial
Intelligence gradually but strongly are moving away from theory and
entering our daily lives. This course focuses on those practical
developments, and what role law and ethics play. We do not limit
ourselves to present technology, but include prophecies on how society
may change in the future and what we can and should do about it. -
Science, Business & Innovation
Programme - Bachelor (University)A future proof society depends on smart and innovative solutions. The Science, Business & Innovation (SBI) bachelor programme at the VU is unique in the Netherlands, because it teaches students to look at the world from a scientific, societal and economic standpoint. You will learn to look beyond the borders of sectors and develop the necessary skills to translate scientific inventions into innovative, market-oriented applications.
The SBI programme consists of a combination of courses from both natural and social sciences, and more business-oriented courses. During the bachelor you will learn to judge both the market value and the societal value of inventions developed in laboratories. You will develop both academic skills, such as critical thinking and dealing with interdisciplinary issues, and entrepreneurial skills, such as working in projects and making strong arguments. With these skills you will be able to develop a business model and take great ideas to the next level. This is serious business. This course is in Dutch.
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Science, Business & Innovation
Programme - MasterThe Master SBI is unique in the Netherlands and is a close collaboration between the Faculty of Sciences, the School of Business and Economics, and the Faculty of Social Sciences. Science, Business and Innovation is a Master’s Programme, offered by VU Amsterdam only.
The Master SBI is a two years programme (120 EC) and is taught in English. All SBI Master students will take general courses in the business aspects of and science behind scientific innovations. Alongside these mandatory courses, students will take specific courses depending on the specialization they choose.
The SBI Master programme offers two thematic specializations: Energy & Sustainability and Life & Health. The energy science specialization focuses on the development and implementation of sustainable solutions, the life science specialization emphasizes on drug development, molecular diagnostics and innovative medical instrumentation.
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Science, Business & Innovation (SBI) for Science Students
Minor - Bachelor (University)This minor is open to undergraduate students of Pharmaceutical Sciences, Medical sciences, Physics, Chemistry. This Minor is only available in Dutch.
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Scientific Computing and Programming
Course - MasterThis course gives an introduction to programming in a scientific research context and consists of a number of modules.
The ‘Introduction to Unix/Linux Systems’ is compulsory. From the two modules on compiled languages the students should choose either ‘Scientific Software Development in Fortran’ or ‘Introduction to C Programming’. A brief description of the currently available modules is given below, but additional modules on high-performance-computing and scientific algorithms are under construction.
1) Introduction to Unix/Linux Systems
Includes logging in; directories and files; grep and regular
expressions; editing with vi; sed and awk; shells and shell programming.2) Scientific Software Development in Fortran
Includes flavors of Fortran; compiling; variables and data types; procedures; reading/writing data; arrays; control statements; modules; user-defined types; structured programming with abstract data types (ADTs); introduction to concepts in software design.3) Introduction to C Programming
Includes compiling with gcc; variables; control structures (e.g. loops); data types and functions; input/output; pointers; basic algorithms.4) Scientific Scripting with Python
Includes introduction to scripting and automation; introduction to Python; running scripts; loading modules; variables; functions; opening/closing files; reading data; extracting data from strings; writing data; running external programs; working with structured data (eg XML, SQL databases); classes and object-oriented programming. -
Scientific Computing and Programming
Course - MasterThis course provides an introduction into modern programming methods used by scientists. Emphasis lies on applications in chemistry, but the programming methods are of course more generally applicable and useful for other scientific fields as well. The study load is 4 weeks net study time (equal to 6 EC) and is spread out equally over a period of 8 weeks thereby assuming 50% availability of the students during this period.
In the first period students learn either the C++ or the Fortran90 programming language and practice their skills with increasingly complex programming assignments. This period is ended with a partial exam in week 5 of the course. The final 3 weeks are dedicated to a programming assignment in which students develop a scientific software application to solve a computational chemistry problem. Contact sessions during these weeks will be organized such that students get individual feedback on their program design and implementation.
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Scientific Programming 1
Course - Bachelor (University)In this course you’ll learn Python, a programming language that is increasingly used by scientists from all fields of study. We focus on the absolute basics of programming, which you will learn while doing programming problems from several scientific areas.
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Scientific Programming 2
Minor - Bachelor (University)This course continues the problem solving curriculum from Scientific Programming 1. You’ll work on larger programs and get to know Python a lot better, so you get ready to learn on your own.
Assistance is provided in the form of online Q&A with other students and our staff, as well as online tutoring. Optionally, you can join a study group to learn together.
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Secure Programming
Course - Bachelor (University)This is an introductory course on computer security. The emphasis
will be on how to develop applications with security in mind. At the
end of the course, students should be familiar with the following:1. Basic concepts in computer security.
2. How common vulnerabilties can be exploited to undermine software
security.
3. How proper design, implementation, and testing can make software more
secure.
4. How cryptography can be used to make software more secure.
5. How automated tools can be used to make software more secure. -
Security of Systems and Networks
Course - MasterPlease refer to the System and Network Engineering web pages for detailed and current course information.
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Service Oriented Design
Course - MasterLearn advanced design techniques applicable to large service-oriented software systems. Be able to select among them and apply them for a specific system. Be able to reason about and assess the design decisions.
The lectures explain the concepts related to the Service Orientation software paradigm and Service Oriented Architecture (SOA). The lectures provide the students with knowledge about how to identify the requirements for a service-oriented software system, how to map them on business services and transform them into complex networks of software services. Special emphasis is given to the design reasoning techniques for crucial decision-making, service identification, SOA design and migration. Experts from academia and industry give guest lectures. The students participate in small teams to develop understanding of various service-oriented aspects, and work on an assigned SOA design project.
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Sets and Combinatorics
Course - Bachelor (University)In this course you will learn: Sets, set operations, the algebra of set theory, the laws of De Morgan, product sets and power sets, standard samples spaces of Probability Theory, basic rules of combinatorics, binomial and multinomial coefficients, binomial and multinomial theorem, cardinality and (un)countability, functions and graphs, principle of complete induction.
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Software Architecture
Course - MasterThis course examines fundamental architecture design decisions that should ensure that a software system is able to achieve as much as possible the quality requirements. This concerns the division of a system into components, the relationships between these components, the quality requirements of the individual components and the system as a whole, and decisions that need to be made to balance between conflicting requirements.
Software Engineering
Information Sciences
Computer Science (Joint Degree)
VU -
Software Engineering
Programme - MasterYou already know how to code. And over the years you’ve gained the necessary theoretical and practical experience. But you want more. You want to take your qualities as a software engineer to the next level. To work with other software engineers on realistic, complicated issues. To solve isolated technical problems, but also to operate within the whole dynamic and extensive field that software engineering is. To not just know the how, but to understand the why. The programme concerns the broad field of software engineering, a field that is in constant movement due to innovations in technology, design patterns and techniques. Software engineering distinguishes itself from classical computer science by its focus on human factors, system size and complexity of requirements.
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Software Engineering
Programme - Bachelor (HBO)Smart applications sometimes require complex software systems. Consider software used by a wholesaler, or a smart web application for a music festival. Software Engineering (formerly Computer Science) is part of the HBO ICT. In Software Engineering you will delve into functional, reliable and user-friendly software systems. Together with other students, you will design and develop your software for education, healthcare, government and organizations in business services. Whilst taking into account the customer’s specific requirements. You will learn to program at the highest level in several programming languages and using the latest development methods. HBO ICT consists of the learning routes: Business IT & Management (BIM), Game Development (GD), Software Engineering (SE), System and Network Engineering (SNE) and Computer Science (CS).
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Software Engineering and Green IT
Track - MasterSoftware engineering applies a systematic and quantifiable approach to the development, execution and maintenance of complex software. Green IT is the study and practice of environmentally sustainable computing. The combination of Software Engineering and Green IT in one track provides the students with the instruments necessary to gain a holistic understanding of large-scale and complex software systems, to manage their evolution, assess their quality and environmental impact, quantify their value and sustainability potential, and organize their development in different local and distributed contexts. Software engineering and Green IT is a broad and comprehensive field, in which engineering plays an important role, next to social, economic and environmental aspects. The field continually evolves, as the types of systems and the world at large do change as well. The field is being influenced by practices and development paradigms such as outsourcing, global software development, service orientation, smart and pervasive computing, and energy-aware software engineering.
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Software Evolution
Course - MasterThis course is designed around lab sessions in which we study real and large (open-source) software systems, written in languages like C, Java, PHP or Ruby. We use Rascal -a programming language workbench, or meta programming language- to apply and build software metrics, software analyses, software visualisations and (if time permits) software transformations. See http://www.rascal-mpl.org. The student is supported with introductory courses and interactive lab sessions while learning this new language in the beginning.
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Software Process
Course - MasterDuring this course you will come to understand why big software engineering projects are prone to failure. You will come to understand how performance is influenced at different levels: that of the individual software engineer, the team and the whole organization. You will learn about motivation, competences, and the crucial role of culture. Also you will learn about organizational paradigms and control mechanisms, quality paradigms, and the role of planning and design in a world that is volatile and of which a lot is unknown. As software engineering is a special kind of organization, you will also learn how effective our best practices are.
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Software Specification, Verification and Testing
Course - MasterSoftware specification, verification, and testing entail checking whether a given software system satisfies given requirements and/or specifications. Without a specification, it is impossible to state what a piece of software should do, and there is no reasonable way to set up the test process. An informal specification is not enough. If we aim to automate the test process we need pre-given information about:
– which tests are relevant –> this information states the preconditions of the code
– what the outcomes of the relevant tests should be –> this information states the postconditions of the code.
Programs written in functional or imperative languages can be tested, given a formal specification, by means of a random test generator. This test method will be illustrated for a number of example programs that are written in Haskell. The course assumes basic familiarity with this language and focusses on how to test programs written in either functional or imperative style, and how to use tools for automated test generation.
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Software Sustainability for Managers
Course - Professional educationModern society is growing increasingly digital. At the same time, it aims to be inclusive, to increase the quality of life, to tune and adapt for the need of different contexts, and last but not least, to decrease its environmental footprint as we learn how to best integrate, apply, and prioritize sustainability.
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Software Testing
Course - MasterThe course is an introduction to software testing with an emphasis on testing techniques. A few automatic testing tools are demonstrated. Prerequisites: a previous course in Software engineering. Programming proficiency in Java.
Computer Science (Joint Degree)
Software Engineering
VU -
Statistical Data Analysis
Course - Bachelor (University)This course acquaints the students with the theory and application of several widely used statistical analysis techniques. After completing this course the student knows the theory behind the different techniques and is able to verify which techniques are applicable to a given data set. Using the learned statistical tools, the student is able to summarize and analyze real data sets using the statistical software package R.
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Statistical Models
Course - MasterThe goals of this course are to get acquainted with some of the most commonly used statistical models, to learn how to apply these models in valid settings, and to understand the basic theory behind these models.
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Statistical Programming in R and Python
Course - MasterThe R program is an important tool in statistical analysis and
programming. In the master program Genes in Behavior and Health (GBH),
master students will be required to use R in GBH courses (e.g., Behavior
Genetics) and in their internships. Looking beyond your present MA
program, experience in using R is also a valuable addition to your C.V,
as R is becoming a standard program both in and beyond academia.The aim of the present course is to teach to practical R skills, within
the context of common statistical analyses and genetics. While the
emphasis is on using R, the context is useful because it will refresh
your statistical knowledge, and introduce you to some genetic concepts.
Following this course, you will be able to conduct data management and
data analyses in R. -
Statistics
Course - MBAAfter this course, the student should be able to: Identify Big Data problems that require statistical techniques; Apply the statistical techniques correctly on Big Data problems; Understand the properties of these techniques, and the role of assumptions; Interpret the conclusions properly; Program in “R”.
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Statistics
Course - Bachelor (University)The course Statistics is a first introduction to the basic concepts of mathematical statistics. After completing this course the student can set up a basic statistical model, estimate parameters in the model, formulate and perform standard hypothesis tests and construct confidence intervals.
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Statistics 1
Course - MasterIn addition to the already known concepts, population and sample,
average, variance and correlation (from Methodology 1), you will also
learn more about variables, probability, frequency distribution,
dependent and independent events. Odds calculation is also included in
the course. Much attention will be given to the question how you can
make statements about a population based on a sample of that population.
In other words, how to calculate confidence intervals and how to apply
statistical tests. You will learn what significance and statistical
power means, and why the size of the sample, the size of differences and
the degree of association matter. You will learn the formulas for the t
test and chi-square and when to apply these. Finally, you will learn
about regression and how this technique is used to answer psychological
and educational questions. -
Statistics 2
Course - MasterDuring Statistics 1, you have learned how to visualise, calculate and
test the differences between two groups or the relationships between two
variables, while also obtaining the confidence intervals. During
Statistics 2 you expand this knowledge. You will learn how to compare
multiple groups and how to analyze the relationships between three or
more variables. In addition, you will learn what is making models
entails. During the parallel tutor groups, you analyze data sets and
learn how to describe the statistical method, display results, and
formulate conclusions. You will independently practise calculations in a
digital learning environment. -
Statistics and SPSS
Course - MasterIn this course, the student is introduced to the basics of statistics.
The student will acquire a basic understanding of techniques used for:
– descriptive statistics, introducing measures and graphical methods of
describing data;
– studying the relationship between two variables, introducing
correlation, chi square and regression analysis;
– statistical inference, starting with the foundation for statistical
inference (random distributions and standard scores) and ending with
estimation and hypothesis testing.The student will learn to use the statistical software program SPSS to
describe and analyse data. By the end of this course, the student should
be able to describe and display data and to draw valid inferences based
on data by using appropriate statistical tools. -
Statistics for Econometric Analysis
Course - Bachelor (University)This course prepares students for (PPLE-)courses in econometrics. Upon completion of this course, students are able to:
- comprehend the basiscs of the theory of probability and the related mathematical methods and techniques;
- comprehend the characteristics of a number of discrete and continuous probability distributions;
- comprehend the distributions of the mean and the variance of a random sample from a population;
- comprehend the distribution of the mean (proportion) of a random sample from a population with a binary distribution;
- comprehend the distribution of the difference of the means and the ratio of the variances of independent random samples from two populations;
- comprehend the distribution of the difference of the means (proportions) of independent random samples from two populations with a binary distribution;
- comprehend the techniques of testing hypothesis and constructing confidence interval estimators of the above mentioned means, differences in means, variances and ratio of variances;
- describe the purpose of each technique and the conditions for its validity, and recognise the circumstances in which each technique can be used;
- interpret and report the results of these techniques correctly;
- comprehend the difference between population and sample characteristics;
- describe the quality of estimators;
- understand basic theory in simple and multiple linear regression: to calculate estimates of the coefficients in simple linear regression models, to correctly perform tests about (a combination of) the coefficients or about the overall fit in multiple regression models, to predict the value of the dependent variable (with its limitations), to include categorical variables as independent variables;
- check for the necessary requirements when using linear regression (regression diagnostics);
- Using EXCEL for calculations in the field of probablilty theory, interval estimation, hypothesis testing, simple multiple regression and multiple linear regression.
Bachelor’s Politics, Psychology, Law and Economics (PPLE)
UvA -
Statistics for Forensic Science
Course - Bachelor (University)An important goal of the course is to provide students with the required knowledge of statistical and probabilistic reasoning to distinguish correct from erroneous argumentation when applied to Forensic Science. Intuitive reasoning is frequently the source of serious misconceptions that all too often have lead to wrong juridical sentences. In the course, the students will see how to recognize and avoid such mistakes through formalistic analysis.
A second goal is to provide students with a basic toolbox for statistical estimation and hypothesis testing. The course is not meant as an advanced statistics course, but we will spend considerable effort on understanding and applying statistical tests such as the standard normal test, the student-t test and – ultimately – the chi-square test.
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Statistics for High-Dimensional Data
Course - MasterThis course gives an overview of modern statistical methods that are
used in the analysis of big or high-dimensional data. Such data usually
comprise a limited number of individuals that have been characterized
with respect to many traits. These data arises in genomics, where
genetic information is measured for many thousands of genes
simultaneously, in functional MRI imaging of
the brain, but also in economic applications.The course covers some of the most important statistical issues for big
or high-dimensional data, including: a) multiple testing, the
family-wise error rate and false discovery rate control; b) shrinkage,
Stein’s estimator; c) penalized estimation, in particular ridge and
lasso regression; and (time-allowing) either d) asymptotic theory, or
e) penalized estimation of covariance matrices.Several types of high-dimensional data will be discussed and used as
examples during the course. In terms of applications the course focuses
on cancer genomics, but theoretical aspects will apply to other fields
as well. -
Statistics for Networks
Course - Bachelor (University)Researchers from diverse disciplines as biology, physics, sociology,
economics, computer science and mathematics, are more and more involved
with the collection, modeling and analysis of network data. The
relational nature of network data means that statistical analysis of
such data is generally more involved than the `standard’ statistical
analysis, that different mathematical models and different statistical
methods are needed, and that different problems need to be faced. The
course focuses on the mathematical aspects of statistical modeling and
statistical analysis of networks, and will touch upon some computational
aspects of network analysis. Topics that will be discussed are:
descriptive statistics for networks, network sampling, network modeling,
inference for networks, and modeling and prediction for processes on
network graphs. -
Statistics for Sciences
Course - MasterIn this course we will cover the basics of data collection and summary, probability theory, statistical inference, and statistical modeling, as used in the natural sciences. The course covers the following topics:
- Categorical data
- Sampling, descriptive statistics and statistical plots
- Probability principles, counting methods, conditional probability, independence and Bayes’ rule
- Random variables, mean, variance, probability mass and density functions, cumulative distribution functions
- Discrete probability distributions: Bernoulli, binomial
- Continuous probability distributions: normal, Student’s t, chi-square, F
- Principles of point estimation: bias, mean square error
- Sampling distributions, the Law of large numbers, the Central limit theorem
- Maximum likelihood estimation
- Confidence intervals for population means or proportions, and differences between means or proportions
- Hypothesis testing for population means or proportions, and differences between means or proportions
- Type I and II error, power, practical significance
- Correlation, Linear regression
- Analysis of Variance
- Using R for data analysis
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Statistics in Linguistics
Course - MasterWe focus on statistical analysis of research data, centred around today’s standard method, which involves linear models, especially those with mixed effects: every time you run an experiment with more than one participant, and record more than one piece of data from each participant, you are likely to need a “mixed-effects model” to make sense of the resulting data. This term refers to the fact that your experimental design contains both “fixed effects” (multiple test conditions per participant, and/or different groups of participants) and “random effects” (participants drawn randomly from a population of people, and often also words or sentences drawn randomly from a language).
If you are already familiar with some statistical techniques, you may have seen t-tests, correlation tests, and analysis of variance. During the course, these concepts turn out to be specific simplifying cases of mixed-effects modelling.
The course also addresses design issues that make the analyses suitable for your experiment, such as sampling, data collection, and reliability and validity of measurements. You apply these concepts, together with the analysis techniques, to theoretical, typological and applied research in your linguistic subdisciplines. Special attention is paid to statistical inference, i.e. correct use and formulation of statistical results.
Master’s General Linguistics (Linguistics)
Research Master’s Linguistics and Communication (Linguistics (research))
UvA -
Statistics in Neurosciences
Course - MasterStatistical data analysis is the process of inspecting, cleaning,
transforming, and modeling data in order to test scientific hypotheses
and answer research questions. The lectures of this course will provide
an overview of quantitative methods that are frequently used in
neuroscience research. These include e.g. correlation, regression,
(paired) t-test, (repeated measures) ANOVA, and multi-level analysis. We
will also discuss concepts like p-values, the multiple testing problem,
Type I and II errors,
sampling, and statistical power. Each lecture will provide the
theoretical background. The practicals and weekly obligatory assignments
will
guide you through a series of tailored research problems that you will
tackle using the statistical package R. You will receive
hands-on experience in the main steps involved in statistical analyses:
from the formulation of hypotheses, selection of the most appropriate
test, checking of assumptions, cleaning of data, and running of
analyses in R, to formally reporting the obtained results. This hands-on
experience is invaluable for the internships in the first and second
year of the Master of Neurosciences, and for your success as an
independent researcher. -
Statistics in R
Course - Bachelor (University)During this course, you combine some skills acquired in the previous two blocks of the BSc Cognition, Language and Communication (1st year) and add the skill of statistical analysis and proper reporting on empirical research. The first week is an introduction to statistics and a `bootcamp’, in which you learn basic methods of statistical analysis in R. In the second week, you will be applying your new statistical skills to your own dataset and learn to write a complete research paper, with special focus on the results section and how it should connect to the discussion and conclusion of a research paper. We will be using the free software R and RStudio.
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Statistics with R
Course - MasterBiologists often have to handle, analyze, and present analysis results
of large sets of biological data, originating from genomics,
transcriptomics, proteomics, and metabolomics experiments. In many
cases, these tasks cannot be performed using standard “press the button”
commercial statistical packages. A popular solution to this problem is
the use of the open source statistical programming environment R. R is
used intensively in the community of experimental biologists, and most
newly published data analysis techniques are first available as
R-packages. -
Statistics, Simulation and Optimization
Course - MasterThis course deals with advanced statistical methods, simulation, and optimization. The student will learn when to use which method, which tooling is appropriate in which situation, and the connections between the different methods. We will also show how these methods fit within the broader framework of data science and analytics.
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Stochastics and Financial Mathematics (SFM)
Programme - MasterIn stochastics we study phenomena in which ‘chance’ plays a role, such as the price of a stock in a financial market, interactions of molecules in a living cell, the evolution of a physical system, etc. The mathematical level of the probability theory and statistics that is relevant in realistic applications is typically quite high, especially in financial mathematics.
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System Optimisation
Course - Professional educationAfter this course, students should be able to:
- optimise deterministic systems:
- being able to model business problems as optimisation problems;
- recognise (Mixed-Integer) Linear Programmes (MILPs);
- use Excel and AIMMS to programme and solve MILPs;
- interpret the results.
- optimise stochastic systems:
- understand the role of uncertainty in business problems;
- understand basic models for capacity planning and the role of uncertainty;
- develop simulation models using simulation software;
- interpret the results.
- optimise deterministic systems:
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Systems Biology in Practice
Course - MasterThe aim of the course is to get acquainted with the interdisciplinary approach of experimental microbial physiology, transcriptome analysis and proteome analysis. Students will learn how information obtained by experiments at the level of cellular behaviour, genetic profile and enzymatic make-up can be combined in order to get insight in the mechanisms underlying regulation and adaptation of microbial organisms. Students will be introduced to the basic techniques and principles of microbial physiology, transcriptome analysis, massspectrometry and data analysis.
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Text Retrieval and Mining
Minor - Bachelor (University)The underlying question behind this course is how a machine collects, represents and processes textual data to algorithmically extract valuable information, identify consistent patterns and learn systematic relationships between pieces of text. The technological topics which will be covered in this course are:
– Textual data collection and indexing;
– text representation;
– text pre-processing;
– machine learning for text classification and ranking;
– evaluation.
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The Social Web
Course - MasterIn this course the students will learn theory and methods concerning communication and interaction in a Web context. The focus is on distributed user data and devices in the context of the Social Web. This course will cover theory, methods and techniques for: personalization for Web applications; Web user & context modelling; user-generated content and metadata; multi-device interaction; and usage of social-web data.
Information Studies (UvA)
Computational Science (Joint degree)
Computer Science
Artificial Intelligence
VU -
Toegepaste Machine Learning
Course - Bachelor (University)The underlying question behind this course is how to algorithmically extract valuable information from raw data. Data Mining is an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data.
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Using R for data wrangling, analysis and visualization
Course - MasterIn this introduction to R, students will first be introduced to the basics of the R environment and language and learn about data types and structures. We will use the Rstudio interface and rely on Rmarkdown for making “reproducible research,” which combines prose, code, and analysis into one document (or slideshow or website). Next we will start to explore our data through aggregation and visualization using packages like ggplot2, and produce professional quality data tables and graphics. We will then move on to “data wrangling,” where data, big and small, will be read, cleaned, combined, and prepared for analysis with packages dplyr and tables. Thereafter, we will learn how to organize complex data analysis processes. Finally, if time permits, we will create some interactive documents and host them online.
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Valuation
Course - Professional educationThis course will cover modern principles and tools of valuation. It will cover discounted cash flow models and relative (multiple-based) valuation, and also briefly introduce contingent claim/option analysis. The course creates a strong foundation for different valuation models by analysing the features and assumptions implicit in any valuation analysis, starting from the term structure of interest rates, estimating discount rates, measuring cash flows and calculating growth rates. Students will conduct a full-fledged valuation exercise through the case assignment. The course also includes discussions of specific companies’ business models, in order to teach students to think critically about how companies present their financial statements and what key characteristics of successful businesses are.
Upon completion of this course students have the following:
- Knowledge:
- compute companies’ values using several different models and techniques;
- analyse companies’ accounting statements and the corresponding management discussion;
- compare the advantages and disadvantages of different valuation techniques, as well as the key assumptions underlying each model. Students will also learn to evaluate which valuation model is appropriate for different types of assets.
- Skills:
- Attitude:
- developing an understanding of how proper valuation affects the societal impact of finance;
- criticising existing companies’ business models during in-class discussions, and assessing whether the company management’s presentation of key financial information is accurate;
- questioning the assumptions underlying key valuation models.
- Knowledge:
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Valuation
Course - MBAThis course focuses on the foundations of finance, with in particular capital budgeting and valuation. The objective of this course is to introduce students to fundamental concepts and to the most commonly used tools in valuation and capital budgeting. The topics covered include: time value of money, capital budgeting valuation of bonds and stocks, the relationship between risk and return, the Capital Asset Pricing Model, market efficiency, capital structure decisions and working capital management. Although these are issues that are relevant in any organization we will specifically discuss in addition how these tools can be used in a health care setting. The course is intended to provide you with both a lasting conceptual framework and, through the incorporation of real-world data and business cases, a greater understanding of how real life situations play out.
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Web Data Processing Systems
Course - MasterThe Web constitutes the largest repository of knowledge that is available to mankind, and its impact on modern society is unprecedented at many levels. Many Web companies are valued with billion dollar quotations and are now central to our modern life.
The key players in the Web industry must face numerous challenges that are concerned with the size, distribution, heterogeneity, and the uncontrolled nature of the Web. Systems to process Web data require the application of a combination of techniques spanning databases, distributed systems, data mining, and artificial intelligence.
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Web Services and Cloud-Based Systems
Course - MasterThis course will introduce students to the principles of web services and cloud systems. Students will learn about the different paradigms of cloud systems (IaaS, PaaS, SaaS), and understand the mechanisms and technologies behind each mode to successfully harness cloud resources. A number of real use case studies of existing cloud systems, and service-based appliations on clouds will be covered during the lectures. The course will also cover more advanced topics such as security of clouds and multi-clouds.