Syllabus

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

Lecture	Slides	Chapter	Extras
00	Introduction		main.m generate_example_data.m plot_flips.m
01	cont.
02	Sum and Product Rules Introduction to Matlab	1	CPT Sum Prod Rule - main.m Intro to Matlab
03	Markov Random Fields & Graphical Models	8
04	Computational Aspects of Discrete and Linear Gaussian Models	8.1
05	Conditional Independence &	8.2-3
06	Inference in Graphical Models & Factor Graphs	8.4
07	cont.
08	Sum product Algorithm (Belief Propagation)
09	cont.
10	cont.
11	K-means, Gaussian Distribution	9.1
12	Gaussian Mixture Models	9.1
12	Expectation Maximization for GMM’s	9.2
13	cont.
14	Generalized EM	9.4
15	cont.
16	EM for linear regression,(pencast)
17	Variational Inference	10.1
18	Variational Inference Cont.	10.1
19	Variational GMM	10.2
20	Variational Inference Usage	10.6
21	Basic sampling methods	11.1
22	Markov chain Monte Carlo	11.2
23	Final project proposal presentations
24	LDA
25	PCA	12.1
26	(Hidden) Markov Models	13.1
27	Forward backward, Viterbi, Sum product again	13.2
28	Linear dynamical systems, Kalman filter	13.3

W4240