WS 2012/2013 (310003) Machine Learning: Unsupervised Methods Lecture and Tutorial Prof. Dr. Laurenz Wiskott

Lecture (2 SWS, 2 credit points): Tuesdays 12:00-13:30 o'clock in the larger INI seminar room NB 3/57. First time 16.10.2012.
Tutorial (2 SWS, 4 credit points): Tuesdays 10:15-11:45 o'clock in the smaller INI seminar room NB 3/72. First time 16.10.2012.

Questions and answers session for Exam 1: Thursday 2013-02-14 15:00-16:00 in NB 3/29.
Exam 1: Monday 2013-02-18 10:00-12:30 in HZO 60.
Exam 2: Wednesday 2013-09-18 11:00-13:30 in NC 5/99.
For the exams, only pen and ruler are permitted plus a handwritten formulary (Formelsammlung) of one page DIN A4 (only one side, not two sides written). Paper will be provided.

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Language: This course can be given in English upon request. Course material (lecture notes and exercise sheets) will be in English in any case.

Goal: (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.

Content: 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.

Requirements: 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, ...).

Certificate: 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.

Schedule

Rules of the course.