| Week | Date | Topic | Lecturer | Pdf | Notes |
| 1. | 17. 2. | Introduction | VF | | |
| 2. | 24. 2. | Predictor evaluation | VF | | |
| 3. | 3. 3. | Empirical Risk Minimization | VF | | |
| 4. | 10.3. | Probably Approximately Correct Learning | VF | | |
| 5. | 17. 3. | Vapnik-Chervonenkis dimension. Structural Risk Minimization. | VF | | |
| 6. | 24. 3. | Generative Learning | VF | | |
| 7. | 31. 3. | Linear Models | VF | | |
| 8. | 7. 4. | Support Vector Machines | VF | | |
| 9. | 14. 4 . | Kernel Methods | 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 | | |