===== Syllabus =====


^Week ^Date ^Topic ^Lecturer  ^Pdf ^Notes ^
|1.|24. 9.| **Introduction** | VF | {{ :courses:be4m33ssu:intro_ws2024.pdf | }} | |
|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 | |[[https://www.stat.cmu.edu/~larry/=stat705/Lecture12a.pdf|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 |