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This page is located in a preparation section till 23.09.2019.

Syllabus

Lecture Date Topic Lecturer Pdf Notes
1.24. 9. Introduction BF
2.1. 10. Empirical risk minimization I VF chap 2 in [1]
3.8. 10. Empirical risk minimization II VF chap 3 in [1]
4.15. 10. Support Vector Machines VF chap 4, 5 in [1]
5.22. 10. Supervised learning for deep networks JD
6.29. 10. Deep (convolutional) networks JD
7.5. 11. Unsupervised learning, EM algorithm, mixture models BF
8.12. 11. Bayesian learning BF
9.19. 11. Hidden Markov Models BF
10.26. 11. Structured output SVMs VF
11.3. 12. Markov Random Fields BF
12.10. 12. Ensembling I JD
13.17. 12. Ensembling II JD
14.7. 1. reserve
courses/be4m33ssu/lectures.txt ยท Last modified: 2019/09/12 11:04 by drchajan