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.
- Course type
- 6 CP
- Winter Term 2017/2018
every week on Tuesday from 12:15 to 13:45 in room NB 3/57.
First appointment is on 10.10.2017
Last appointment is on 30.01.2018
every week on Tuesday from 10:30 to 12:00 in room NB 3/57.
First appointment is on 17.10.2017
Last appointment is on 31.01.2018
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.