courses:be4m33ssu:lectures [CourseWare Wiki]

Syllabus

Lecture	Date	Topic	Lecturer	Pdf	Notes
1.	20. 9.	Introduction	BF
2.	27. 9.	Predictor evaluation and learning via using empirical risk	VF	, to print:	[1] Chap 2, [2] Chap 7
3.	4. 10.	Empirical risk minimization	VF	, to print:	[1] Chap 2, [2] Chap 7
4.	11. 10.	Empirical risk minimization II	VF	, to print:	[1] Chap 4, [2] Chap 12
5.	18. 10.	Structured Output Support Vector Machines	VF	, to print:	[1] Chap 5, [2] Chap 12
6.	25. 10.	Supervised learning for deep networks	JD
7.	1. 11.	SGD, Deep (convolutional) networks	JD	SGD Deep ANNs
8.	8. 11.	Generative learning, Maximum Likelihood estimator	BF
9.	15. 11.	EM algorithm, Bayesian learning	BF
10.	22. 11.	Hidden Markov Models I	BF		This lecture is held at Dejvice campus, room T2_C3-340
11.	29. 11.	Hidden Markov Models II	BF
12.	6. 12.	Ensembling I	JD		[4]
13.	13. 12.	Ensembling II	JD		[2] Chap 10
14.	10. 1.	Q&A	All