Warning
This page is located in archive. Go to the latest version of this course pages. Go the latest version of this page.

Syllabus

Lecture Date Topic Lecturer Pdf Notes
1.21. 9. Introduction BF
2.28. 9. — National holiday —
3.5. 10. Empirical risk VF (to print ) [1] Chap 2, [2] Chap 7
4.12. 10. Empirical risk minimization VF (to print ) [1] Chap 2, [2] Chap 7
5.19. 10. Empirical risk minimization II VF (to print ) [1] Chap 4, [2] Chap 12
6.26. 10. Structured Output Support Vector Machines VF (to print ) [1] Chap 5, [2] Chap 12
7.2. 11. Supervised learning for deep networks JD
8.9. 11. SGD, Deep (convolutional) networks JD SGD Deep networks
9.16. 11. Generative learning, Maximum Likelihood estimator BF
10.23. 11. EM algorithm, Bayesian learning BF takes place in KN:A-214
11.30. 11. Hidden Markov Models BF
12.7. 12. Markov Random Fields BF Contents of this lecture are not examined
13.14. 12. Ensembling I JD [4]
14.4. 1. Ensembling II JD [2] Chap 10
courses/be4m33ssu/lectures.txt · Last modified: 2022/01/04 10:42 by drchajan