Schedule
| Week | Date | Topic |
| 1 | Mar 07 | Introduction / Basic Principles [M] pdf |
| 2 | Mar 12 | Dimensionality Reduction [S]
pdf
zip
Chapter 10 (Marsland) |
| 3 | Mar 21 | Classification (Intro) [M]
pdf
Chapter 1+4 (Bishop) |
| 4 | Mar 28 | Probability and Learning [S]
pdf
zip
Chapter 8 (Marsland) |
| 5 | Apr 04 | Support Vector Machines [M]
pdf
Chapter 5 (Marsland) Chapter 6+7 (Bishop) |
| 6 | Apr 11 | Learning with Trees [S]
pdf
zip
Chapter 6 (Marsland) |
| 7 | Apr 18 | Ostern |
| 8 | Apr 25 | Ostern |
| 9 | May 02 | Ensemble Learning [M]
pdf
Chapter 7 (Marsland) Chapter 14 (Bishop) Chapter 8+16 (Hastie) |
| 10 | May 09 | Unsupervised Learning 1 [M]
pdf
Chapter 9 (Bishop) Chapter 8 (Marsland) Chapter 14 (Hastie) |
| 11 | May 16 | Unsupervised Learning 2 [M]
pdf
Chapter 9 (Marsland) Chapter 14 (Hastie) |
| 12 | May 23 | Neuronal Networks [S]
pdf
zip
Chapter 2+3 (Marsland) |
| 13 | May 30 | Text Mining [S] pdf zip |
| 14 | Jun 06 | Evolutionary Learning [S]
pdf
zip
Chapter 12 (Marsland) |
| 15 | Jun 13 | Graphical Models [S]
pdf
zip
Chapter 15 (Marsland) |
| 16 | Jun 20 | Linear Regression [M]
pdf
Chapter 3 (Bishop) |
| 17 | Jun 27 | Final
Previous exams pdf pdf
|
no lecture
subject to modifications
due