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.

Prof. Dr. Laurenz Wiskott Lecturer

(+49) 234-32-27997 laurenz.wiskott@ini.rub.de NB 3/29

Course type

Lectures

Credits

6 CP

Term

Winter Term 2018/2019

Lecture

Takes place every week on Tuesday from 12:15 to 13:45 in room NAFOF 04/493.
First appointment is on 09.10.2018
Last appointment is on 29.01.2019

Exercise

Takes place every week on Tuesday from 10:30 to 12:00 in room NAFOF 04/493.
First appointment is on 16.10.2018
Last appointment is on 29.01.2019

Preliminary meeting

Takes place every week on Tuesday from 9:00 to 10:30 in room NAFOF 04/493.
First appointment is on 16.10.2018
Last appointment is on 29.01.2019

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, ...).

Goals: (i) The students should get to know a number of unsupervised learning methods. (ii) They should be able to discuss which of the methods might be appropriate for a given data set. (iii) They should understand the mathematics of these methods.

The course is given in English. It will be concluded with an oral exam. The dates will be set in the last lecture.

Literature: For most topics a script will be available.