===== Syllabus =====


^Lecture ^Date ^Topic ^Lecturer  ^Pdf ^Notes ^
|1.|26. 9.| **Introduction** | BF | {{:courses:be4m33ssu:stat_mach_learn_l01.pdf| }} | |
|2.|3. 10.| **Predictor evaluation** | VF | {{ :courses:be4m33ssu:er_ws2023.pdf | }}, print: {{ :courses:be4m33ssu:er_ws2023_print.pdf | }}| [1] Chap 2, [2] Chap 7 |
|3.|10. 10.| **Empirical risk minimization**  | VF | {{ :courses:be4m33ssu:erm_ws2023.pdf | }}, print: {{ :courses:be4m33ssu:erm_ws2023_print.pdf | }} | [1] Chap 2, [2] Chap 7 | 
|4.|17. 10.| **Probably Approximately Correct Learning** | VF |{{ :courses:be4m33ssu:pac_ws2023.pdf | }}, print: {{ :courses:be4m33ssu:pac_ws2023_print.pdf | }} | [1] Chap 4, [2] Chap 12 |
|5.|24. 10.| **Structured Output Support Vector Machines** | VF | {{ :courses:be4m33ssu:sosvm_ws2023.pdf | }}, print: {{ :courses:be4m33ssu:sosvm_ws2023_print.pdf | }} | [1] Chap 5, [2] Chap 12 |
|6.|31. 10.| **Supervised learning for deep networks** | JD | {{ :courses:be4m33ssu:anns_ws2023.pdf | }} | |
|7.|7. 11.| **SGD, Deep (convolutional) networks** | JD | {{ :courses:be4m33ssu:sgd_ws2023.pdf | }} {{ :courses:be4m33ssu:deep_anns_ws2023.pdf | }} | |
|8.|14. 11.| **Generative learning, Maximum Likelihood estimator** | BF | {{ :courses:be4m33ssu:gener_ml.pdf | }} |[[https://www.stat.cmu.edu/~larry/=stat705/Lecture12a.pdf|L. Wasserman, Exp. Fam. ]] |
|9.|21. 11.| **EM algorithm, Bayesian learning** | BF | {{ :courses:be4m33ssu:em_bayesian.pdf | }}|will be held in KN:A-320 |
|10.|28. 11.| **Hidden Markov Models I**  | BF | {{ :courses:be4m33ssu:hmms.pdf | }}|  |
|11.|5. 12.| **Hidden Markov Models II** | BF | {{ :courses:be4m33ssu:hmms2.pdf | }}| |
|12.|12. 12.| **Ensembling I** | JD | {{ :courses:be4m33ssu:ensembling_ws2023.pdf | }} | [4] |
|13.|19. 12.| **Ensembling II** | JD |  | [2] Chap 10 |
|14.|9. 1.| **Q&A** | All |  | |