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Lecture | Slides | Chapter | Extras |
---|---|---|---|
00 | Introduction | Intro to Matlab my_regress.m CH01PR19.txt plot_gpa_fit.m |
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01 | Sum and Product Rules Bayes Nets |
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02 | cont. | ||
03 | Computational Aspects of Discrete and Linear Gaussian Models | 8.1 | |
04 | Conditional Independence & Markov Random Fields |
8.2-3 | |
05 | Inference in Graphical Models & Factor Graphs | 8.4 | |
06 | cont. | ||
07 | Sum product Algorithm (Belief Propagation) | ||
08 | cont. | ||
09 | cont. | ||
10 | K-means clustering and Gaussian Mixture Models | 9.1 | |
11 | Expectation Maximization for GMM’s | 9.2 | |
12 | cont. | ||
13 | Generalized EM |
9.4 | |
14 | cont. | ||
15 | EM for linear regression,(pencast) | ||
16 | Variational Inference | 10.1 | |
17 | Variational Inference Cont. | 10.1 | |
18 | Variational GMM | 10.2 | |
19 | Variational Inference Usage | 10.6 | |
20 | Basic sampling methods | 11.1 | |
21 | Markov chain Monte Carlo | 11.2 | Neal tech. report |
22 | cont. | 11.3 | |
23 | PCA | 12.1 | |
24 | (Hidden) Markov Models | 13.1 | |
25 | Forward backward, Viterbi, Sum product again | 13.2 | |
26 | Linear dynamical systems, Kalman filter | 13.3 | |
27 | Particle filtering |