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Syllabus

Week Date Topic Lecturer Pdf Notes
1.24. 9. Introduction VF
2.1. 10. Supervised learning for deep networks JD
3.8. 10. Predictor evaluation VF (print ) [1] Chap 2, [2] Chap 7
4.15. 10. Empirical risk minimization VF (print ) [1] Chap 2, [2] Chap 7
5.22. 10. Probably Approximately Correct Learning VF (print ) [1] Chap 4, [2] Chap 12
6.29. 10. dean's day
7.5. 11. SGD, Deep (convolutional) networks JD SGD deep nets
8.12. 11. Support Vector Machines VF
9.19. 12. Ensembling I JD [4], lecture moved to KN:A-320
10.26. 1. Ensembling II JD [2] Chap 10
11.3. 12. Generative learning, Maximum Likelihood estimator VF 2025-01-15 Errata: slide 14, error in cumulant of Bernoulli
12.10. 12. EM algorithm, Bayesian learning VF
13.17. 12. Hidden Markov Models I JD [5] Chap 17
14.7. 12. Hidden Markov Models II JD
courses/be4m33ssu/lectures.txt · Last modified: 2025/01/14 16:19 by xfrancv