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Tentative Class Schedule

	Date	Topic	Reading
01.	Sept 03	Introduction	Notes, Bishop Ch. 1
02.	Sept 08	Linear Discriminant, Estimation (MLE/MAP/Bayes)	Bishop Ch. 4.1, 4.2, 2.2, 2.3, 2.4 Notes
03.	Sept 10	Intro to MATLAB and MALT Generative Models, Naive Bayes	Mitchell Chapter, Bishop Ch. 4.2
04.	Sept 15	Discriminative Models, Logistic Regression	Bishop Ch. 4.3, 4.5, Minka paper, Ng-Jordan paper
05.	Sept 17	Perceptrons	Bishop Ch. 4.1.7
		HW1 (due Sept 26)
06.	Sept 22	Neural Networks I (both lectures)	Bishop Ch. 5.1, 5.2, 5.3
07.	Sept 24	Neural Networks II
08.	Sept 29	Decision Trees	Mitchell Ch. 3
09.	Oct 01	Convex Analysis and Optimization I (both lectures)	Boyd, Vandenberghe Ch. 2.1-2.3, 3.1-3.3
		HW2 (due Oct 10)
10.	Oct 06	Convex Analysis and Optimization II	Boyd, Vandenberghe Ch. 4.1-4.4,5.1-5.5
11.	Oct 08	Support Vector Machines I	Bishop Ch. 7.1, Burges Tutorial
12.	Oct 13	Support Vector Machines II
13.	Oct 15	Review
14.	Oct 20	Midterm
15.	Oct 22	Kernel Methods I	RKHS, Representer Theorem, Paper
16.	Oct 27	Kernel Methods II
17.	Oct 29	Learning Theory I	Mitchell Ch. 7, Andrew Moore's Tutorial, Burges Tutorial Devroye-Gyorfi-Lugosi Ch 12, 13
18.	Nov 03	Learning Theory II	Notes
		HW3 (due Nov 14)
19.	Nov 05	Online Learning I	Mitchell Ch. 7.5, Blum survey(Sections 1,2)
20.	Nov 10	Online Learning II	EG paper
21.	Nov 12	Boosting I (both lectures)	Adaboost Paper, Tutorial, Bishop Ch. 14.2. 14.3
22.	Nov 17	Boosting II
23.	Nov 19	Clustering: kmeans, EM I (both lectures)	Bishop Ch. 9.1, 9.2
24.	Nov 24	Clustering: kmeans, EM II	Bishop Ch. 9.3, 9.4
		HW4 (due Dec 5)
25.	Nov 26	Graph Cuts, Spectral Clustering I (both lectures)	Tutorial
26.	Dec 01	Graph Cuts, Spectral Clustering II
27.	Dec 03	Principal Component Analysis	Bishop Ch. 12.1
28.	Dec 08	Semi-supervised Learning	Survey
29.	Dec 10	Review
	Dec 15	Final (4-6pm)