Lectures

Week Date Topic Lecturer Pdf Notes
1. 17. 2. Introduction VF
2. 24. 2. Predictor evaluation VF Begginer's guide
3. 3. 3. Empirical Risk Minimization VF Begginer's guide
4. 10.3. Probably Approximately Correct Learning VF Begginer's guide
5. 17. 3. Vapnik-Chervonenkis dimension. Structural Risk Minimization. VF Begginer's guide
6. 24. 3. Generative Learning VF
7. 31. 3. Linear Models VF
8. 7. 4. Kernel Methods VF
9. 14. 4 . Support Vector Machines VF
10.21. 4. Unsupervised Learning VF
11.28. 4. Bayesian Learning VF
12.5. 5. VF
13.12. 5. VF
14.19. 5. Deep Learning and Generalization VF