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Syllabus

Lecture Date Topic Lecturer Pdf Notes
1.24. 9. Introduction BF
2.1. 10. Empirical risk VF , ( printable: ) ch 2 in [1]
3.8. 10. Empirical risk minimization VF , ( printable: ) ch 2 in [1], ch 3 in [1]
4.15. 10. Support Vector Machines I VF , ( printable: ) ch 4 in [1], ch 12 in [2]
5.22. 10. Support Vector Machines II VF , (printable: ) ch 5 in [1]
6.29. 10. Supervised learning for deep networks JD
7.5. 11. Deep (convolutional) networks JD , SGD
8.12. 11. Unsupervised learning, EM algorithm, mixture models BF
9.19. 11. Bayesian learning BF
10.26. 11. Hidden Markov Models BF
11.3. 12. Markov Random Fields BF for additional reading (not part of the exam)
12.10. 12. Ensembling I JD
13.17. 12. Ensembling II JD
14.7. 1. reserve
courses/be4m33ssu/lectures.txt · Last modified: 2019/12/10 12:14 by drchajan