===== Syllabus =====


^Lecture ^Date ^Topic ^Lecturer  ^Pdf ^Notes ^
|1.|24. 9.| Introduction | BF | {{:courses:be4m33ssu:stat_mach_learn_l01_ws19.pdf| }} | | 
|2.|1. 10.| Empirical risk | VF | {{ :courses:be4m33ssu:erm1_ws2019_full.pdf | }}, ( printable: {{ :courses:be4m33ssu:erm1_ws2019.pdf | }})  | ch 2 in [1] |
|3.|8. 10.| Empirical risk minimization  | VF | {{ :courses:be4m33ssu:erm2_ws2019_full.pdf | }}, ( printable: {{ :courses:be4m33ssu:erm2_ws2019.pdf | }}) | ch 2 in [1], ch 3 in [1] | 
|4.|15. 10.| Support Vector Machines I | VF | {{ :courses:be4m33ssu:svm1_ws2019_full.pdf | }}, ( printable: {{ :courses:be4m33ssu:svm1_ws2019.pdf | }}) | ch 4 in [1], ch 12 in [2] |
|5.|22. 10.| Support Vector Machines II | VF | {{ :courses:be4m33ssu:svm2_ws2019_full.pdf | }}, (printable: {{ :courses:be4m33ssu:svm2_ws2019.pdf | }}) | ch 5 in [1] |
|6.|29. 10.| Supervised learning for deep networks | JD | {{ :courses:be4m33ssu:anns_ws19.pdf | }} | |
|7.|5. 11.| Deep (convolutional) networks | JD | {{ :courses:be4m33ssu:deep_anns_ws19.pdf | }}, {{ :courses:be4m33ssu:sgd_ws19.pdf |SGD}}  | |
|8.|12. 11.| Unsupervised learning, EM algorithm, mixture models | BF | {{ :courses:be4m33ssu:emalg_ws2019.pdf | }} | |
|9.|19. 11.| Bayesian learning | BF | {{ :courses:be4m33ssu:bayes-learn-ws2019.pdf | }} |  |
|10.|26. 11.| Hidden Markov Models | BF | {{ :courses:be4m33ssu:hmms-ws2019.pdf | }} | |
|11.|3. 12.| Markov Random Fields  | BF | {{ :courses:be4m33ssu:mrfs-ws2019.pdf | }}  | for additional reading (not part of the exam)|
|12.|10. 12.| Ensembling I | JD | {{ :courses:be4m33ssu:ensembling-ws2019.pdf | }} |  |
|13.|17. 12.| Ensembling II | JD |  |  |
|14.|7. 1.| reserve |  |  |  |