===== Syllabus =====


^Lecture ^Date ^Topic ^Lecturer  ^Pdf ^Notes ^
|1.|20. 9.| **Introduction** | BF | {{:courses:be4m33ssu:stat_mach_learn_l01.pdf| }} | |
|2.|27. 9.| **Predictor evaluation and learning via using empirical risk** | VF | {{ :courses:be4m33ssu:er_ws2022.pdf | }}, to print: {{ :courses:be4m33ssu:er_ws2022_print.pdf | }}| [1] Chap 2, [2] Chap 7 |
|3.|4. 10.| **Empirical risk minimization**  | VF | {{ :courses:be4m33ssu:erm_ws2022.pdf | }}, to print: {{ :courses:be4m33ssu:erm_ws2022_print.pdf | }} | [1] Chap 2, [2] Chap 7 | 
|4.|11. 10.| **Empirical risk minimization II** | VF | {{ :courses:be4m33ssu:erm2_ws2022.pdf | }}, to print: {{ :courses:be4m33ssu:erm2_ws2022_print.pdf | }} | [1] Chap 4, [2] Chap 12 |
|5.|18. 10.| **Structured Output Support Vector Machines** | VF | {{ :courses:be4m33ssu:sosvm_ws2022.pdf | }}, to print: {{ :courses:be4m33ssu:sosvm_ws2022_print.pdf | }} | [1] Chap 5, [2] Chap 12 |
|6.|25. 10.| **Supervised learning for deep networks** | JD | {{ :courses:be4m33ssu:anns_ws2022.pdf | }} | |
|7.|1. 11.| **SGD, Deep (convolutional) networks** | JD | {{ :courses:be4m33ssu:sgd_ws2022.pdf |SGD}} {{ :courses:be4m33ssu:deep_anns_ws2022.pdf | Deep ANNs}} | |
|8.|8. 11.| **Generative learning, Maximum Likelihood estimator** | BF | {{ :courses:be4m33ssu:gener_ml.pdf | }} | |
|9.|15. 11.| **EM algorithm, Bayesian learning** | BF | {{ :courses:be4m33ssu:em_bayesian-ws2022.pdf | }}|
|10.|22. 11.| **Hidden Markov Models I**  | BF | {{ :courses:be4m33ssu:hmms.pdf | }}| This lecture is held at Dejvice campus, room T2_C3-340 |
|11.|29. 11.| **Hidden Markov Models II** | BF | {{ :courses:be4m33ssu:hmms2.pdf | }}| |
|12.|6. 12.| **Ensembling I** | JD | {{ :courses:be4m33ssu:ensembling_ws2022.pdf | }} | [4] |
|13.|13. 12.| **Ensembling II** | JD |  | [2] Chap 10 |
|14.|10. 1.| **Q&A** | All |  | |