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
1.22. 9. Introduction BF
2.29. 9. Empirical risk VF (printable ) [1] Chap 2, [2] Chap 7
3.6. 10. Empirical risk minimization VF (printable ) [1] Chap 2, [2] Chap 7
4.13. 10. Support Vector Machines I VF (printable ) [1] Chap 4, [2] Chap 12
5.20. 10. Support Vector Machines II VF (printable ) [1] Chap 5, [2] Chap 12
6.27. 10. Supervised learning for deep networks JD
7.3. 11. SGD, Deep (convolutional) networks JD
8.10. 11. Generative learning, EM algorithm BF
9.17. 11. National holiday
10.24. 12. Bayesian learning BF
11.1. 12. Hidden Markov Models BF
12.8. 12. Markov Random Fields BF
13.15. 12. Ensembling I JD
14.5. 1. Ensembling II JD
courses/be4m33ssu/lectures.txt · Last modified: 2020/12/15 11:56 by drchajan