This course covers a variety of unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, self-organizing maps, growing neural gas, Bayesian theory and graphical models. We will also briefly discuss reinforcement learning. Criteria for a certificate for the tutorial are an active participation, in particular presentation of selected exercises, and at least 50% in the final exam. This course can be given in English upon request. Course material (lecture notes and exercise sheets) will be in English in any case.

Course type

Lectures

Term

Winter Term 2014/2015

Lecture

Takes place every week on Tuesday from 12:15 to 13:45 in room NB 3/57.
First appointment is on 07.10.2014

Exercise

Takes place every week on Tuesday from 10:30 to 12:00 in room NB 3/57.
First appointment is on 14.10.2014

The mathematical level of the course is mixed but generally high. The tutorial is almost entirely mathematical. Mathematics required include calculus (functions, derivatives, integrals, differential equations, ...), linear algebra (vectors, matrices, inner product, orthogonal vectors, basis systems, ...), and a bit of probability theory (probabilities, probability densities, Bayes' theorem, ...).