Machine Learning: Unsupervised Methods
Lecture and TutorialProf. Dr. Laurenz Wiskott
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Machine Learning: Unsupervised MethodsLecture and TutorialProf. Dr. Laurenz Wiskott |
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Lecture (2 SWS, 2 credit points): Tuesdays 12:15-13:45 in the larger INI seminar
room NB 3/57. First time 7.10.2014.
Tutorial (2 SWS, 4 credit points): Tuesdays 10:30-12:00 in the larger INI seminar
room NB 3/57. First time 14.10.2013.
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Language: This course will be given in English.
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. (ii) 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, ...).
Exam: The course will be concluded with an oral exam. The dates will be set in the last lecture.
| # | date | topic |
| - | UNSUPERVISED LEARNING | |
| 1 | 07.10.2014 | Introductory remarks Principal component analysis I |
| 2 | 14.10.2014 | Principal component analysis II |
| 3 | 21.10.2014 | Principal component analysis III |
| 4 | 28.10.2014 | Independent component analysis I |
| 4/5 | 04.11.2014 | Independent component analysis I/II |
| 5 | 11.11.2014 | Independent component analysis II |
| 6 | 18.11.2014 | Vector quantization |
| 7 | 25.11.2014 | Clustering |
| 7/8 | 02.12.2014 | Clustering Self Organizing Maps |
| 8 | 09.12.2014 | Slow Feature Analysis |
| 9 | 16.12.2014 | Bayesian inference |
| 9/10 | 13.01.2015 | Bayesian inference Inference in Bayesian networks |
| 10/11 | 20.01.2015 | Inference in Bayesian networks Inference in Gibbsian networks |
| 12 | 27.01.2015 | Learning in Bayesian networks |
| - | REINFORCEMENT LEARNING | |
| 13 | 03.02.2015 | Reinforcement Learning |