===== Syllabus =====


^Lecture ^Date ^Topic ^Lecturer  ^Pdf ^Notes ^
|1.|22. 9.| Introduction | BF | {{:courses:be4m33ssu:stat_mach_learn_l01.pdf| }} | | 
|2.|29. 9.| Empirical risk | VF | {{ :courses:be4m33ssu:er_zs2020.pdf | }} (printable {{ :courses:be4m33ssu:er_zs2020_print.pdf | }}) | [1] Chap 2, [2] Chap 7 |
|3.|6. 10.| Empirical risk minimization  | VF | {{ :courses:be4m33ssu:erm_zs2020.pdf | }} (printable {{ :courses:be4m33ssu:erm_zs2020_print.pdf | }})| [1] Chap 2, [2] Chap 7 | 
|4.|13. 10.| Support Vector Machines I | VF | {{ :courses:be4m33ssu:svm1_ws2020.pdf | }} (printable {{ :courses:be4m33ssu:svm1_ws2020_print.pdf | }}) | [1] Chap 4, [2] Chap 12 |
|5.|20. 10.| Support Vector Machines II | VF | {{ :courses:be4m33ssu:svm2_ws2020.pdf | }} (printable {{ :courses:be4m33ssu:svm2_ws2020_print.pdf | }}) | [1] Chap 5, [2] Chap 12 |
|6.|27. 10.| Supervised learning for deep networks | JD | {{ :courses:be4m33ssu:anns_w2020.pdf | }} | |
|7.|3. 11.| SGD, Deep (convolutional) networks | JD | {{ :courses:be4m33ssu:sgd_w2020.pdf | }} {{ :courses:be4m33ssu:deep_anns_w2020.pdf | }} | |
|8.|10. 11.| Generative learning, EM algorithm | BF | {{:courses:be4m33ssu:ml-em.pdf| }}  | |
|9.|17. 11.| National holiday  |  |  |  |
|10.|24. 12.| Bayesian learning | BF | {{:courses:be4m33ssu:bayes-learn-ws2020.pdf| }} | |
|11.|1. 12.| Hidden Markov Models  | BF | {{:courses:be4m33ssu:hmms-ws2020.pdf| }}  | |
|12.|8. 12.| Markov Random Fields | BF | {{ :courses:be4m33ssu:mrfs-ws2020.pdf | }} |  |
|13.|15. 12.| Ensembling I | JD | {{ :courses:be4m33ssu:ensembling_w2020.pdf | }} |  |
|14.|5. 1.| Ensembling II | JD |  |  |