Machine Learning: Basic CourseLecture and Analytical TutorialProf. Dr. Laurenz Wiskott and Jun. Prof. Dr. Christian Igel |
Analytical Tutorial: Tuesdays 12:15-13:45 o'clock in the upper INI
seminar room ND 03/89,92.
Lecture: Tuesdays 14:15-15:45 o'clock in the upper INI seminar room ND
03/89,92.
Description: 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, deep-belief networks, and Markov random fields.
Language: The course will be given in English upon request.
Literature: For many topics a script will be available, other literature will be mentioned in the lecture.
Prerequisites: Good command of linear algebra and calculus, basic knowledge of probability theory.
# | date | topic | lecturer |
1 | 13.10.2009 | Introductory remarks Principal component analysis I | Wiskott |
2 | 20.10.2009 | Principal component analysis II | Wiskott |
3 | 27.10.2009 | Principal component analysis III Fisher discriminant analysis | Wiskott |
4 | 03.11.2009 | Independent component analysis I | Wiskott |
5 | 10.11.2009 | Independent component analysis II | Wiskott |
6 | 17.11.2009 | Vector quantization | Wiskott |
7 | 24.11.2009 | Clustering | Wiskott |
8 | 01.12.2009 | Slow Feature Analysis Bayesian inference | Wiskott |
9 | 08.12.2009 | Inference in Bayesian networks | Wiskott |
10 | 15.12.2009 | Inference in Gibbsian networks | Wiskott |
11 | 22.12.2009 | Learning in Bayesian networks | Wiskott |
12 | 12.01.2010 | Optimization | Wiskott |
13 | 19.01.2010 | Markov-Random-Fields and Boltzmann Machines | Igel |
14 | 26.01.2010 | dito | Igel |
15 | 02.02.2010 | dito | Igel |
Exam 1: Mon, 22.02.2010, 10:00-12:30 o'clock in ND 3/99.
Exam 2: Wed, 22.09.2010, 10:00-12:30 o'clock in ND 3/99
Only pen, ruler, and 1 DIN A4 sheet of handwritten formulary (Formelsammlung) are permitted. Paper will be provided.