Courses & Programmes

Many medical informaticians are working with large and complex epidemiological databases, and are trying to reconstruct knowledge from such sources. Almost always this requires statistical data-analyses which will be complicated through multidimensionality, nonlinearity, and confounding factors. In this course some modern statistical techniques will be discussed, studied, and applied.

The aim is that students are capable of analyzing complex data with minimal guidance: they will recognize how data were sampled, and which consequences the sampling design has for the data-analysis; they will realize which assumptions are made with specific statistical techniques, and how those can be checked; and students are aware of the instability of estimated statistical models and how such instability can be quantified.

This 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.

The 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.

The 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.

This course is part of the Mastermath programme. All information can be found at the Mastermath website.

Machine 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.​

Please refer to the System and Network Engineering web pages for detailed and current course information.

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.

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.

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.

Please refer to the System and Network Engineering web pages for detailed and current course information.

In practice, one will collect data in a timely and efficient manner, to analyze, interpret and present. This requires knowledge of statistical techniques.

In this module we are introduced to theory and applications of some commonly used statistical techniques. After completing this module datasets can be summarized and analyzed in a coherent and relevant way.

Please note that this course cannot be followed separately.

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.

Have 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.

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:

1. self-driving cars;
2. social network analysis;
3. teaching computers to understand text;
4. image and face recognition;
5. targeted advertising;
6. 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:

• www.meetup.com/Amsterdam-Data-Science/

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

In 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.

This 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.

This 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.

The 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.

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: 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.

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.

This 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.

Machine learning is marking a revolution in the world. From an academic research topic, over the last decade it has shift to a major paradigm 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 enabling medical diagnoses.

In this course we study learning 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 supervised classification and unsupervised clustering, which we extend to deep learning networks. We have application lectures on text processing, computer vision and recommender systems.

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 off the important mathematical concepts, and in the lab you will gain hands-on experience through a number of coding assignments (implementing your own deep learning functions) and by participating in a Kaggle competition or project. Finally, a few experts from the field (both academic as well as industry colleagues) are invited for giving guest lectures.

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.

The 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.

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.

Archival 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.

The AI Minor is open to Bachelor students in Computer Science, Information, Multimedia and Management, and Lifestyle Informatics. It includes courses in Logic and Uncertainty, Semantic Web, Collective Intelligence, Information Retrieval and Heuristics.

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.

At the core of Data Science are methods for analysis of large volumes of data. Recently much more data has become available in electronic form, methods for analysis and modelling these data for prediction, classification and optimisation have become much more effective. Recent technical innovations, such as Deep Learning, provide increasingly powerful tools that make it possible to find complex patterns in very large datasets.

Much of the Master’s AI is about Data Science. The obligatory courses on Machine Learning address key technology and theory for modelling large amounts of data. The courses on Machine Learning, Natural Language Processing, Information Retrieval and Computational Intelligence all have a strong focus on data-driven methods.

For the “AI courses” in the curriculum, students can choose advanced courses on these topics: Machine Learning 2, Computer Vision 2, Natural Language Processing 2, Information Retrieval 2, Deep Learning, Data Mining Techniques, Information Visualisation and Probabilistic Robotics. All these courses are about modelling data. These can be complemented by courses outside AI, for example on distributed computer systems, privacy and ethical questions, or on statistics.

Many applications of AI involve the use of the World-wide Web. The WWW makes information from around the world available. The ability to find information in this enormous resource and to combine different pieces of information to answer complex questions is a challenge for AI. Data available from the web make it possible to find the context of a query and thereby produce a better ranking. The solution will be in combining information search and retrieval with reasoning methods.

The Master’s programme AI makes it easy to take this as a focus. AI courses for this specialisation are Information Retrieval 2, Knowledge Representation on the Web and Knowledge Engineering. These courses extend and deepen the knowledge from the first year courses Information Retrieval 1 and Knowledge Representation. The courses Natural Language Processing 2 and Applied Language Technology further extend this knowledge to natural language interactions and to machine translation, enabling queries to be answered from information in multiple languages.

The 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.

Immerse 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.

Large amounts of data often contain a wealth of information. Traditional techniques are not sufficient in order to extract this information. Data Mining and Machine Learning techniques from the field of Artificial Intelligence are specifically designed to detect, in a data-driven manner, automatic patterns. In this course you will learn state-of-the-art machine learning algorithms for classification (e.g., decision trees, random forests, SVM), regression (e.g., model trees, regression trees), clustering (e.g., k-means), and association rules (APRIORI) are treated in an applied setting.

Please note that this course cannot be followed separately.

The focus of this track is on the application of AI in Socially Aware Computing, highlighting the analysis and application of new AI techniques to develop solutions that understand and can reason about their social context. Its goals are highly diverse, and range from optimizing internet searches to supporting elderly people in their struggle with dementia. You will learn how human behavior can be interpreted based on sensor data and computational models of physiological and cognitive processes. You will gain experience in integrating such models in dedicated applications that support humans in their daily lives, making these systems truly aware of human functioning.

This 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.

This 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.

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.

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.

Understanding 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.

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.

This 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.

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

Big 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.

The 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.

In 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.

Two-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.

This minor provides basic training in data science for students at the bachelor level. Students will use R studio on datasets, with results visualized in dashboards. Business plans are part of the minor, with contributions and case studies from the engineering sector.

In 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.

After this course, the student should be able to:

– Understand the functionality and limitations of different databases;

– Traditional relational databases (“row-stores”);

– Modern analytical relational databases (“column-stores”, “NewSQL”);

– Modern non-relational databases (“key-value stores”, “NoSQL”);

– Databases designed for big data analytics;

– Understand data management tools;

– Employ databases in big data analytics

After 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).

In 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.

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.

Research 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.

Observations 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.

In 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.

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.

The 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.

The 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.

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.

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.

This 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.

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.

You will tackle quantitative business problems with the aid of mathematical algorithms which are then implemented in decision support systems.

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.

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.

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.

An essential part of the Operations Management program is to expose the students to actually apply the knowledge they have on modelling and optimization techniques using the computer. During the course, students work together in small groups on selected cases that originate from practice.

Please refer to the System and Network Engineering web pages for detailed and current course information.

In this course, students will acquire hands-on experience in designing, running, analyzing, and modeling experiments for the cognitive sciences. The course will be organized around one specific topic, that leads to a research question, an hypothesis, an experimental design, analysis plan and a formal modeling approach.

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.

The course consists of two parts: reading research papers, and doing a small project.

The 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.

Complexity 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.

You simulate the human brain in a computer program to make decisions with optimization and data mining techniques.

The overall aim of this course is to provide knowledge about concepts, theory, and techniques used in computational intelligence and the know-how to employ these for making intelligent machines. In particular, to enable students to:- gain profound understanding of fundamental computational intelligence concepts, algorithms, and their implementation;- understand the theoretical background of proposed solutions;- develop skills in the use of computational intelligence and to demonstrate this in physical robots or virtual creatures;- appreciate relevant current research topics in the theory and practice of computational intelligence.

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.

Understanding 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.

Computer 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.

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.

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.

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).

The Web & Media track centers on the innovative application of the web to retrieve, disclose, and share information, in particular in media-rich settings. How can you make the World Wide Web intelligent so that it becomes much more easy to represent, process and share electronic information and knowledge across companies and communities of interest? How do you design multimedia databases for broad user groups on the Internet on, say, some pop music style or museum art collection, including videoclips, sound samples, explanatory notes, and an easily searchable discography or collection overview?

The 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.

Digital 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).

The 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.

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.

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.

Digital 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.

Data Analytics is a booming term that is used for the use of large amounts of data to: gain knowledge, optimize operations, and explore markets. An example is the use of real-time traffic data to analyze vehicle movements, to predict congestions, to find the fastest route, and to schedule maintenance operations. Underlying data analytics is a series of methods and tools that include querying databases, using multivariate statistics, and visualizing high-dimensional data. This course will address theoretical and practical aspects in a number of selected topics relating to data analytics.

The following approaches to data analysis will be covered: Templates, write-ups and illustrative examples; Overview of tools for data analysis; Obtaining data: Finding data sets and Web scraping, file formats; Data manipulation techniques: Data quality, reshaping data, appending and joining data sets; Plotting and visualization: Exploration and presentation; Exploratory data analysis: Visual inspection, descriptive analytics, insights; Estimation techniques: Multiple approaches based on assumptions, sampling basics.

This 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.

Data is indubitably the most important trend in media work today. In an increasingly complex datafied society, data journalism represents the last frontier of reporting and storytelling. Data – such as the information generated by ‘open data’ government policies, academic research, digital archiving, and interactions in social networks – can be used to tell stories. But only skilled data analysts can bridge the gap between journalistic and technical knowledge to find the stories hidden in data. This course introduces methods of data analysis and presentations against the backdrop of journalistic processes and values. Students will learn how to critically and empirically work with data, how to tell interesting stories with data sets, and how to present findings in meaningful ways. Aspects of data journalism to be addressed include the collection, cleansing, analysis, and visualization of data, as well as digital storytelling and publishing. Students will work in teams to ‘reverse engineer’ existing data journalism productions, and to work on original products.

This course is part of the minor Big Data in Urban Technology. Students will learn various techniques used in data analysis such as clustering, classification and principal components. This course is in Dutch.

This 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.

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.

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.

In 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.

The 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.

The demand for data science talent is growing enormously. If you are working as a business professional and you are ambitious to dive into data science, this hands-on course might be your stepping stone towards becoming a junior data scientist.

This 8-day hands-on data science course will be lectured by university lecturers and experienced consultants. The consultants have been conducting data science projects for multiple years and have a wide range of practical experiences. They will share their best practices with you and warn you about the pitfalls of data science projects. The academic rigor with regards to research methods and statistics, vital for effective data science projects, will be taught by experienced academic lecturers from the University of Amsterdam.

Four-day hands-on course on Data Science for the audit profession. Auditors use data science to improve the efficiency, effectiveness and added value of their audits.

This 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.

Big 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.

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.

This 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.

After 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.

After this course, students will be able to explain the perceptual principles that help guide the design of visualisations that communicate the intended message clearly. You will be able to explain how Gestalt laws influence the way people view and interpret visual displays of data, how people extract information from graphs, and what limitations are of the human visual system. In particular, you will be familiar with the Cleveland-McGill hierarchy, misleading and distorting effects of commonly made visualisation errors, perceptually effective encoding of quantities and categories, Tufte’s design principles, and Gestalt laws. You will be able to leverage these insights to enhance and optimise graphical display of data for efficient communication. You will have learned to work with various visualisation tools are popular in the data science community. These include R/ggplot, Tableau, and Shiny. As storytelling with graphs and charts often requires the rearrangement and slicing and dicing of data tables, you will also have learned to work with various data manipulation techniques using R, Excel, and SQL.

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.

Students 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.

The data revolution has big implications for society and offers both threats and opportunities for organizations. Big data is seen as a cause of disruptive change. In this module we will look at the impact on organizations of big data. We treat innovation on three levels (process, product/service and ecosystem), and investigate the impact of this. Attention is also paid to how, and how much, you set-up your organization’s data-driven innovation, in order to properly respond to the opportunities.

Please note that this course cannot be followed separately.

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.

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.

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 and in a ‘living case’ business setting. These core concepts are based on the relational data model and SQL – as the de facto standard database language – combined with data visualisation and the design of metrics and dashboards. The course includes a significant practical part with a focus on data modeling, on SQL and on data visualisation to solve business issues.

Deep 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.

• 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

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.

The 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.

Digital data and computational techniques are fundamentally changing the world around us, and the way humanities research is conducted. Humanities scholars have become aware that the new analytical methods and tools can help us to enhance the performance and impact of humanities research. Computer scientists have discovered that the fuzzy data and the hermeneutic methods of humanities research offer daring challenges to computer science. That is why companies like IBM or Samsung are now undertaking collaborations with humanities researchers. As most of the knowledge-intensive jobs have come to depend more on computer technology, university students who master new, sophisticated analytic skills will develop promising career opportunities. Taking digital humanities courses will be exciting, challenging and fun for both humanities and computer science students. The current climate of excitement and innovation around digital humanities will surely have positive effects on teachers and students alike!

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.

It 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.

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.

The 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.

The 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.

After 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.

The 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!

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.

The 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.

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.

This 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.

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.

The objective of this course is to learn about entrepreneurship, with a focus on IT, and especially business ideas that involve Data Science and/or Analytics.

This course consists of several elements:
– lectures about different aspects of entrepreneurship;
– guest lectures by for example successful entrepreneurs and investors in starting companies;
– writing a business plan for a real or imaginary company.

For students who have the intention to start their own company we will make it possible to pitch their ideas for venture capitalists (like a Dragons’s Den). Presence during the lectures is compulsory.
The course will be given by Enno Masurel (specialized in Entrepreneurship, FEWEB) and Ger Koole (Analytics, FEW), assuring that all aspects of IT entrepreneurship will be covered.

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.

Please refer to the System and Network Engineering web pages for detailed and current course information.

The 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.

The 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.

This 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.

Statistical and econometric models are used in various parts of modern portfolio theory and international finance. Therefore, a working knowledge of these models is indispensable for a reliable quantitative assessment of these issues. This course develops skills for the econometric analysis of macroeconomic and financial data, with a particular emphasis on regression theory and time series analysis. The methods taught will enable students to read and interpret empirical literature, which is required for many subjects in international finance.

At the end of the course, students will have:

• familiarity with the linear regression model, including the ability to test for any deviation from its assumptions, and to find proper solutions;
• a working knowledge of several extensions of the basic model, in particular, panel data and time series models (e.g. AR, MA, ARMA);
• the ability to carry out an empirical project: interpreting estimation and testing results (e.g. from a regression package in Python) and building a satisfactory empirical model;
• the ability to judge the corresponding estimation and testing results from empirical literature.

You 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.

Unique 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.

After 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.

This 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.

This 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.

All humanities disciplines are confronted with more and more digital material. This digital material can be studied as individual objects or descriptions of events, but from a digital perspective they can also be considered as data points. Digital methods allow researchers to study relations between objects form a different perspective and on a larger scale. How can humanities researchers use digital data to support their research? What digital tools are at their disposal and how can these tools provide new perspectives and research questions? This course looks at digital data sources and tools that are relevant to the humanities from a programmatic perspective.

Interested 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.

Data 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.

Data 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.

Game theory and operations research (OR) provide methods for network
analysis. Noncooperative game theory studies how rational agents make
strategic decisions. Cooperative game theory is often used as a decision
support system in valuation problems. OR provides methods and algorithms
for computing optimal solutions. In this minor, these techniques will be
used in network optimization problems. To prepare you for that, this
course discusses basic methods and models of game theory and OR
networks.

The course introduces the student to the following topics:

• Elementary graph theory, introducing the basic mathematical concepts
used to uniformly model most problems concerning networks;
• An algorithmic view on network optimization, while introducing basic
optimization problems like shortest paths, minimum spanning tree, and
max flow ;
• Basic network properties like large components and short diameters;
• Introduction to noncooperative games, including normal games,
extensive form games, dominant strategies, Nash equilibrium, and subgame
perfect equilibrium;
• Introduction to cooperative games, including transferable utility
games, the Core, and the Shapley value;
• Applications of games to network analysis.

IMPORTANT: This module introduces students to the fundamentals of
network analysis from the perspectives of (econometrics and) operations
research. It is required for students with a background in either
economics or business, whereas students with a background in
(econometrics and) operations research should NOT attend this module.

Learn 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.

Students 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.

Researchers 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.

In 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.

In the age of information and (big) data, source material has become more diverse and the analysis of data far more complex. This (re)use of source material is creating essentially new data with meaning and significance far beyond the individual sources. This course explores the contexts of use, practical methods and theoretical implications of contemporary information analytics, in particular in a research context. What types of research questions and research methods are emerging in digital humanities? How does this change research methods and practices? How are traditional knowledge institutions catering for research needs beyond traditional access? What other sources and tools are emerging? How to apply practical analytical methods to information and archival problems?

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.

The 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).

Building 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.

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.

Information 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.

Information 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

Information 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

In 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.

Gaining 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.

Everyone is online. For organisations this means that the possibilities to generate and use data are growing. In the Information, Multimedia and Management (IMM) programme at the VU, this is exactly what you will learn to do. You will, for example, learn to develop and implement a search engine, or a museum website that knows what a visitor wants to see. As a student in the IMM programme you will learn about new applications and markets, and create innovative, user-friendly possibilities. You will work closely with teachers and fellow students on all sorts of practice-based projects. The teachers all have broad working experience in public and commercial sectors, both nationally and internationally. This education programme is dynamic because the focus is on new developments. You will learn both communicative and technical skills, and – especially in the third year – you will work on a real assignment in the field at an inspiring organisation. This course is in Dutch.

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.

Internet 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.

This is a course on advanced topics in computer networking. The context  will span broadly from the network core, across the Internet, all the way to the network edge. Students will learn about the recent/current developments in computer networks and the concept of the main technologies (data center networking, software defined networking, network function virtualization, in-network computing, video streaming/analytics, Internet-of-Things, edge computing, etc) that are currently being adopted. After this course, students will understand the design of these technologies and be able to reason about the design choices therein. Students are also expected to build a network system by applying some of the designs in a simulated environment, and analyze and evaluate the merits and limitations of the designs.

Please refer to the System and Network Engineering web pages for detailed and current course information.

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.

The course Introduction to Behavioural Data Science is an introductory course within the master’s track Behavioural Data Science. The course provides a general overview of the structure of a data science project, and a training on several practical skills that are necessary to carry out the different stages of such a project. This encompasses a training on interview techniques, data wrangling within R, an introduction to SQL, statistical modelling, data visualisation within a single spreadsheet, and some principles about reporting results of data science project.

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.

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

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.

The 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.

Computer programming has become an essential skill in science. Some form of programming is often required for all phases of research, from creating your experimental task to analyzing your data – and everything in between. The aim of this course is to teach the basics of computer programming to students with no prior experience. The programming language used in this course is Python, which is widely used in psychology (particularly for processing large datasets, such as from EEG and MRI studies), as well as other science and engineering fields.

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.

Introduction 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.

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.

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.

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.

In 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.

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.

The 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.

Knowledge 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.

Since 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.

In 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.

After 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

Please refer to the System and Network Engineering web pages for detailed and current course information.

This 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).

After 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.

After completing this course, students should:

– have knowledge of the (international) principles and values concerning privacy and data protection;

– have knowledge of the basics of (international) privacy laws, including the Privacy Regulation;

– be aware of the possible threats and risks of big data for privacy;

– have knowledge of the technologies can help to avoid or minimise these risks and threats;

– be aware of the available methods and standards to design privacy-friendly systems and services (Privacy by Design);

– be able to communicate properly and transparently towards data subjects;

– be able to perform a big data maturity scan to assess the level of compliance within the organisation;

– be able to apply privacy principles and values in real-life cases.

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).

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.

Vast 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.

In the bachelor programme Lifestyle Informatics you learn to create tools and applications that make the daily life of people better, safer, and healthier. Some examples of this are:

– an intelligent living environment for elderly people,

– smart training programmes for athletes, or

– intelligent sensors and smart devices to reduce stress.

Lifestyle Informatics applications are entering our daily lives more and more, such as care robot Alice and apps to help people cope with depression. Please note: this course is in Dutch.

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 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.

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.

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.

After 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.

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.

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.

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).

Machine 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.

Gain 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.

Machine learning is a combination of statistics and computer science. Consequently, the course covers both the statistical foundations as well as the practical implementation of machine learning techniques, while applying them to common finance and business use cases. The techniques that are covered include linear and nonlinear methods, Ridge, Lasso, decision trees, random forests, k-nearest neighbor, Logistic, support vector machines, k-means clustering, hierarchical clustering, DBSCAN, several types of neural networks (feed-forward, convolutional, recurrent, and generative adversarial) as well as reinforcement learning. Furthermore, the course covers cross-validation methods to separate chance findings from statistical patterns, discusses methods to deal with high-dimensional data (shrinkage, principle component analysis, non-negative matrix factorization), and methods to improve the performance of those algorithms (bootstrapping, bagging, boosting). Practical business applications to which we apply these methods using Python include, among others, fraud detection, loan decisions, customer segmentation, sentiment and backtesting trading strategies, and the pricing derivatives as well as object and face recognition using neural networks.

This 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.

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.

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 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 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.

This 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.

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.

The 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.

After 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).

After 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).

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.

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.

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.

Mathematics 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.

Mathematics 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.

Research 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.

This 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.

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.

A 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.

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.

In 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.

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.

For a long time relational databases dominated the area. Since the rise of the cloud and big-data databases are larger and become more distributed, also will have other ideas about data storage and data access gained much ground. The alternatives are also called NoSQL or modern databases. In this course students get acquainted with a number of examples of this modern databases.

This course is only avaliable in Dutch

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.

This 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.

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.

Please refer to the System and Network Engineering web pages for detailed and current course information.

Please refer to the System and Network Engineering web pages for detailed and current course information.

The minor where Theory meets Practice in Business. Are you a quant? And are you ready for testing your quantitative skills and apply your mathematical knowledge on real-life challenges in business operations? Then this could be the minor for you! The minor for quantitative decisions making in business. The Department of Econometrics and Operations Research of the Vrije Universiteit Amsterdam offers this minor in collaboration with the department of Information, Logistics and Innovation in the fall semester (September-January) starting in the academic year 2016-2017. Real-life challenges in business operations by applying mathematical analytical methods and techniques from Operations Research and Operations Management are at the core of this minor. By an exciting set of carefully selected courses and business cases, in this minor you will explore the spectrum of analytics skills required for becoming successful in decision making in business. These skills range from identify problems requiring managerial action and translating managerial decisions into mathematical models, to applying, designing and programming algorithms for solving the resulting mathematical problems, to eventually drawing managerial conclusions taking into account behavioral aspects. After finishing your minor, you will be ready for better decision making in business with your quantitative talent as a firm basis.

The minor is aimed at a mixture of students from Econometrics and Operations Research (EOR) and students from Business Administration (BA) with a strong quantitative interest. However, any student in the Netherlands and abroad with an interest in applying mathematics in a business environment should be interested in this minor. Specifically, students from all over the world in Applied Mathematics (AM), and Industrial Engineering (IE) are more than welcome to join.

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.