Lecture | Date | Topic | Lecturer | Pdf | Notes |
1. | 20. 9. | Introduction | BF | | |
2. | 27. 9. | Predictor evaluation and learning via using empirical risk | VF | , to print: | [1] Chap 2, [2] Chap 7 |
3. | 4. 10. | Empirical risk minimization | VF | , to print: | [1] Chap 2, [2] Chap 7 |
4. | 11. 10. | Empirical risk minimization II | VF | , to print: | [1] Chap 4, [2] Chap 12 |
5. | 18. 10. | Structured Output Support Vector Machines | VF | , to print: | [1] Chap 5, [2] Chap 12 |
6. | 25. 10. | Supervised learning for deep networks | JD | | |
7. | 1. 11. | SGD, Deep (convolutional) networks | JD | SGD Deep ANNs | |
8. | 8. 11. | Generative learning, Maximum Likelihood estimator | BF | | |
9. | 15. 11. | EM algorithm, Bayesian learning | BF | | |
10. | 22. 11. | Hidden Markov Models I | BF | | This lecture is held at Dejvice campus, room T2_C3-340 |
11. | 29. 11. | Hidden Markov Models II | BF | | |
12. | 6. 12. | Ensembling I | JD | | [4] |
13. | 13. 12. | Ensembling II | JD | | [2] Chap 10 |
14. | 10. 1. | Q&A | All | | |