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 [1] Chap 2, [2] Chap 7
4.15. 10. Empirical risk minimization VF [1] Chap 2, [2] Chap 7
5.22. 10. Probably Approximately Correct Learning VF [1] Chap 4, [2] Chap 12
6.29. 10. dean's day
7.5. 11. Support Vector Machines VF
8.12. 11. SGD, Deep (convolutional) networks JD
9.19. 11. Generative learning, Maximum Likelihood estimator VF L. Wasserman, Exp. Fam.
10.26. 11. EM algorithm, Bayesian learning VF ???will be held in KN:A-320
11.3. 12. Hidden Markov Models I JD
12.10. 12. Hidden Markov Models II JD
13.17. 12. Ensembling I JD [4]
14.7. 1. Ensembling II JD [2] Chap 10
courses/be4m33ssu/lectures.txt · Last modified: 2024/09/15 11:12 by xfrancv