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Syllabus

Lecture Date Topic Lecturer Pdf Notes
1.26. 9. Introduction BF
2.3. 10. Predictor evaluation VF , print: [1] Chap 2, [2] Chap 7
3.10. 10. Empirical risk minimization VF , print: [1] Chap 2, [2] Chap 7
4.17. 10. Probably Approximately Correct Learning VF , print: [1] Chap 4, [2] Chap 12
5.24. 10. Structured Output Support Vector Machines VF , print: [1] Chap 5, [2] Chap 12
6.31. 10. Supervised learning for deep networks JD
7.7. 11. SGD, Deep (convolutional) networks JD
8.14. 11. Generative learning, Maximum Likelihood estimator BF L. Wasserman, Exp. Fam.
9.21. 11. EM algorithm, Bayesian learning BF will be held in KN:A-320
10.28. 11. Hidden Markov Models I BF
11.5. 12. Hidden Markov Models II BF
12.12. 12. Ensembling I JD [4]
13.19. 12. Ensembling II JD [2] Chap 10
14.9. 1. Q&A All
courses/be4m33ssu/lectures.txt · Last modified: 2023/12/12 11:17 by drchajan