courses:be4m33ssu:lectures [CourseWare Wiki]

Syllabus

Lecture	Date	Topic	Lecturer	Notes
1.	3.10.	Introduction	BF
2.	10.10.	Empirical risk minimization I	VF	chap 2 in [1]
3.	17.10.	Empirical risk minimization II	VF	chap 3 in [1]
4.	24.10.	Support Vector Machines	VF	chap 4, 5 in [1]
5.	31.10.	Supervised learning for deep networks	JD
6.	7.11.	Deep (convolutional) networks	JD
7.	14.11.	Unsupervised learning, EM algorithm, mixture models	BF
8.	21.11.	Bayesian learning	BF
9.	28.11.	Hidden Markov Models	BF
10.	5.12.	Markov Random Fields	BF
11.	12.12.	Structured output SVMs	VF
12.	19.12	Ensembling I	JD
13.	2.1.	Ensembling II	JD
14.	9.1.	Reserve