===== Syllabus =====


^Lecture ^Date ^Topic ^Lecturer  ^Pdf ^Notes ^
|1.|3.10.| Introduction | BF | {{:courses:be4m33ssu:stat_mach_learn_l01.pdf| }} | | 
|2.|10.10.| Empirical risk minimization I| VF | {{:courses:be4m33ssu:erm1_ws2017.pdf| }} | chap 2 in [1] |
|3.|17.10.| Empirical risk minimization II | VF | {{:courses:be4m33ssu:erm2_ws2017.pdf| }} | chap 3 in [1] | 
|4.|24.10.| Support Vector Machines | VF | {{:courses:be4m33ssu:svm_ws2017.pdf| }} | chap 4, 5 in [1] |
|5.|31.10.| Supervised learning for deep networks | JD | {{:courses:be4m33ssu:anns_ws2017.pdf| }} | |
|6.|7.11.| Deep (convolutional) networks | JD | {{:courses:be4m33ssu:deep_anns_ws2017.pdf| }} | |
|7.|14.11.| Unsupervised learning, EM algorithm, mixture models | BF | {{courses:be4m33ssu:emalg_ws2017.pdf| }} | |
|8.|21.11.| Bayesian learning | BF | {{courses:be4m33ssu:bayes-learn-ws2017.pdf| }} |  |
|9.|28.11.| Hidden Markov Models | BF | {{courses:be4m33ssu:hmms-ws2017.pdf| }} | |
|10.|5.12.| Markov Random Fields | BF | {{courses:be4m33ssu:mrfs-ws2017.pdf| }} | |
|11.|12.12.| Structured output SVMs | VF | {{:courses:be4m33ssu:sosvm_2017.pdf| }}  | |
|12.|19.12| Ensembling I | JD | {{:courses:be4m33ssu:ensembling_ws2017.pdf| }} |  |
|13.|2.1.| Ensembling II | JD |  |  |
|14.|9.1.| Reserve | |  | |