| Lecture | Date | Topic | Lecturer | Pdf | Notes |
| 1. | 22. 9. | Introduction | BF | | |
| 2. | 29. 9. | Empirical risk | VF | (printable ) | [1] Chap 2, [2] Chap 7 |
| 3. | 6. 10. | Empirical risk minimization | VF | (printable ) | [1] Chap 2, [2] Chap 7 |
| 4. | 13. 10. | Support Vector Machines I | VF | (printable ) | [1] Chap 4, [2] Chap 12 |
| 5. | 20. 10. | Support Vector Machines II | VF | (printable ) | [1] Chap 5, [2] Chap 12 |
| 6. | 27. 10. | Supervised learning for deep networks | JD | | |
| 7. | 3. 11. | SGD, Deep (convolutional) networks | JD | | |
| 8. | 10. 11. | Generative learning, EM algorithm | BF | | |
| 9. | 17. 11. | National holiday | | | |
| 10. | 24. 12. | Bayesian learning | BF | | |
| 11. | 1. 12. | Hidden Markov Models | BF | | |
| 12. | 8. 12. | Markov Random Fields | BF | | |
| 13. | 15. 12. | Ensembling I | JD | | |
| 14. | 5. 1. | Ensembling II | JD | | |