Lecture | Date | Topic | Lecturer | Pdf | Notes |
1. | 21. 9. | Introduction | BF | | |
2. | 28. 9. | — National holiday — | | | |
3. | 5. 10. | Empirical risk | VF | (to print ) | [1] Chap 2, [2] Chap 7 |
4. | 12. 10. | Empirical risk minimization | VF | (to print ) | [1] Chap 2, [2] Chap 7 |
5. | 19. 10. | Empirical risk minimization II | VF | (to print ) | [1] Chap 4, [2] Chap 12 |
6. | 26. 10. | Structured Output Support Vector Machines | VF | (to print ) | [1] Chap 5, [2] Chap 12 |
7. | 2. 11. | Supervised learning for deep networks | JD | | |
8. | 9. 11. | SGD, Deep (convolutional) networks | JD | SGD Deep networks | |
9. | 16. 11. | Generative learning, Maximum Likelihood estimator | BF | | |
10. | 23. 11. | EM algorithm, Bayesian learning | BF | | takes place in KN:A-214 |
11. | 30. 11. | Hidden Markov Models | BF | | |
12. | 7. 12. | Markov Random Fields | BF | | Contents of this lecture are not examined |
13. | 14. 12. | Ensembling I | JD | | [4] |
14. | 4. 1. | Ensembling II | JD | | [2] Chap 10 |