Syllabus

Note: in order to play the pencast files you need to download and install livescribe desktop.

Lecture	Slides	Chapter	Extras
1/17	Introduction		Tracking, Gambling, Text
1/19	Sum and Product Rules Introduction to Matlab	1	CPT Sum Prod Rule - main.m Intro to Matlab
1/24	Markov Random Fields
1/26	Graphical Models	8
1/31	Computational Aspects of Discrete and Linear Gaussian Models	8.1
2/2	Conditional Independence &	8.2-3
2/7	Inference in Graphical Models & Factor Graphs	8.4
2/9	cont.
2/14	Sum product Algorithm (Belief Propagation)
2/16	Bayesian Inference		main.m generate_example_data.m plot_flips.m
2/21	K-means, Gaussian Distribution	9.1
2/23	Gaussian Mixture Models	9.1
2/28	Expectation Maximization for GMM’s	9.2
3/1	Generalized EM	9.4
3/6	EM for linear regression,(pencast)
3/8	Variational Inference	10.1
3/20	Variational Inference Cont.	10.1	main.m plotMVNIsosurfaces.m plotIsosurfaces.m
3/22	Variational GMM	10.2
3/27	Variational Inference Usage	10.6
3/29	Basic sampling methods	11.1
4/3	Guest Lecture: MCMC	11.2
4/5	Guest Lecture: MCMC	11.2
4/10	LDA
4/12	In class project presentations. Update on progress.
4/17	LDA cont. Gibbs Sampling
4/19	(Hidden) Markov Models	13.1
4/24	ISEDHMM / Sequence Memoizer
4/26	No Class

W4240