Written exam (in case of a small number of participants: oral tests)

The focus of this lecture is on algorithmic and theoretical aspects of statistical
machine learning. We will cover many of the standard algorithms, learn about
the general principles for building good machine learning algorithms, and analyze
their theoretical and statistical properties. The following topics will be
covered: Supervised machine learning, for example linear methods; regularization;
SVMs; kernel methods. Bayesian decision theory, loss functions,
Unsupervised learning problems, for example dimension reduction, kernel PCA,
multi-dimensional scaling, manifold methods; spectral clustering and spectral
graph theory.
Introduction to statistical learning theory: no free lunch theorem; generalization
bounds; VC dimension; universal consistency;
Evaluation and comparison of machine learning algorithms.
Advanced topics in statistical learning, for example low rank matrix completion,
compressed sensing, ranking, online learning.

ML- 4201 Students get to know the most important classes of statistical machine learning
algorithms. They understand why certain algorithms work well and others
don’t. They can evaluate and compare the results of different learning algorithms.
They can model machine learning applications and get a feeling for
common pitfalls. They can judge machine learning algorithms from a theoretical
point of view.

The literature for this lecture will be provided at the beginning of the semester. / Students need to know the contents of the basic math classes, in particular
linear algebra and probability theory.