Syllabus

Week Date Topic Lecturer Pdf Notes
1.23. 9. Introduction VF
2.30. 9. Predictor evaluation. Empirical Risk Minimization VF [1] Chap 2, [2] Chap 7
3.7. 10. Probably Approximately Correct Learning VF [1] Chap 2, [2] Chap 7
4.14. 10. Vapnik-Chervonenkis dimension VF [1] Chap 4, [2] Chap 12
5.21. 10. Supervised learning for deep networks JD
6.28. 10. state holidays
7.4. 11. SGD, Deep (convolutional) networks JD SGD deep nets
8.11. 11. Support Vector Machines VF
9.18. 11. Ensembling I JD moved to KN:A-312, [4]
10.25. 11. Ensembling II JD [2] Chap 10
11.2. 12. Generative learning, Maximum Likelihood estimator VF
12.9. 12. EM algorithm, Bayesian learning VF
13.16. 12. Hidden Markov Models I JD [5] Chap 17
14.6. 1. Hidden Markov Models II JD
courses/be4m33ssu/lectures.txt · Last modified: 2025/12/09 10:42 by xfrancv