| Week | Date | Topic | Lecturer | Pdf | Notes |
| 1. | 23. 9. | Introduction | VF | | |
| 2. | 30. 9. | Predictor evaluation. Empirical Risk Minimization | VF | | [1] Chap 2, [2] Chap 7 |
| 3. | 7. 10. | Probably Approximately Correct Learning | VF | | [1] Chap 2, [2] Chap 7 |
| 4. | 14. 10. | Vapnik-Chervonenkis dimension | VF | | [1] Chap 4, [2] Chap 12 |
| 5. | 21. 10. | Supervised learning for deep networks | JD | | |
| 6. | 28. 10. | state holidays | | | |
| 7. | 4. 11. | SGD, Deep (convolutional) networks | JD | SGD deep nets | |
| 8. | 11. 11. | Support Vector Machines | VF | | |
| 9. | 18. 11. | Ensembling I | JD | | moved to KN:A-312, [4] |
| 10. | 25. 11. | Ensembling II | JD | | [2] Chap 10 |
| 11. | 2. 12. | Generative learning, Maximum Likelihood estimator | VF | | |
| 12. | 9. 12. | EM algorithm, Bayesian learning | VF | | |
| 13. | 16. 12. | Hidden Markov Models I | JD | | [5] Chap 17 |
| 14. | 6. 1. | Hidden Markov Models II | JD | | |