courses:be4m33ssu:lectures [CourseWare Wiki]

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