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 | | [1] Chap 2, [2] Chap 7 |
4. | 15. 10. | Empirical risk minimization | VF | | [1] Chap 2, [2] Chap 7 |
5. | 22. 10. | Probably Approximately Correct Learning | VF | | [1] Chap 4, [2] Chap 12 |
6. | 29. 10. | dean's day | | | |
7. | 5. 11. | Support Vector Machines | VF | | |
8. | 12. 11. | SGD, Deep (convolutional) networks | JD | | |
9. | 19. 11. | Generative learning, Maximum Likelihood estimator | VF | | L. Wasserman, Exp. Fam. |
10. | 26. 11. | EM algorithm, Bayesian learning | VF | | ???will be held in KN:A-320 |
11. | 3. 12. | Hidden Markov Models I | JD | | |
12. | 10. 12. | Hidden Markov Models II | JD | | |
13. | 17. 12. | Ensembling I | JD | | [4] |
14. | 7. 1. | Ensembling II | JD | | [2] Chap 10 |