Courses & Programmes

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.