| Week | Date | Topic | Lecturer | Pdf | Notes |
| 1. | 24. 9. | Introduction | VF | | |
| 2. | 1. 10. | Supervised learning for deep networks | JD | | |
| 3. | 8. 10. | Predictor evaluation | VF | (print ) | [1] Chap 2, [2] Chap 7 |
| 4. | 15. 10. | Empirical risk minimization | VF | (print ) | [1] Chap 2, [2] Chap 7 |
| 5. | 22. 10. | Probably Approximately Correct Learning | VF | (print ) | [1] Chap 4, [2] Chap 12 |
| 6. | 29. 10. | dean's day | | | |
| 7. | 5. 11. | SGD, Deep (convolutional) networks | JD | SGD deep nets | |
| 8. | 12. 11. | Support Vector Machines | VF | | |
| 9. | 19. 12. | Ensembling I | JD | | [4], lecture moved to KN:A-320 |
| 10. | 26. 1. | Ensembling II | JD | | [2] Chap 10 |
| 11. | 3. 12. | Generative learning, Maximum Likelihood estimator | VF | | 2025-01-15 Errata: slide 14, error in cumulant of Bernoulli |
| 12. | 10. 12. | EM algorithm, Bayesian learning | VF | | |
| 13. | 17. 12. | Hidden Markov Models I | JD | | [5] Chap 17 |
| 14. | 7. 12. | Hidden Markov Models II | JD | | |