Probabilistic Graphical Models 
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See CG schedule for CG's lecture notes.
Material covered 
Slides 
Readings /
References


Module 0: Introduction 

L#12 
KF: 1, 2.1, 2.3? (J: 3.1)


Module 1: Directed Graphical Models Bayesian networks: representation/semantics, learning, inference 

L#3  5 


KF: 3
Intro Bayesian Net Local Belief Net 
L#6  8 
Framework: Frequentist, Bayesian 
KF: 15, 16  
L#9 
Framework: Frequentist 
KF: 1818.2  
L#10 
Constraintbased; ChowLiu; Fixedorder; Structure search 
KF: 17 

L# 11 
Variable elimination 
KF: 8.18.3; 8.7.1  
Module 2: Undirected Graphical Models Markov random fields, Factor graphs 

L#1214 

KF: 4  4.4, 4.6 KF: 4.5 KF: 9, Wikipedia: Junction Tree Alg 

Module 3: Approximate Inference  
L#15 
Global Approximate Inference: Inference as Optimization

KF: 10.13, 10.5  
L#1617 (31 Oct, 3 Nov) 
Lay of the Land Presentations 

L#18 
ParticleBased Approximate Inference

KF: 11.14 

Module 4: Learning Revisited 

L#19 

KF: 19, (J: 9, 20) Jordan et al., "Thin Junction Trees" S. DellaPietra, V. DellaPietra and J. Lafferty, "Inducing Features of Random Fields" 

L#20 

KF: 18, KF: 19.3.3
Other slides 

Module 5: Gaussian and Hybrid Models  
L#21 L#22 

KF: 5.5, KF: 6 KF: 5.5.1, KF Hybrid Network Chapter handout (get at Main CS Office, 2nd floor Ath Hall) 

L#2324 (28 Nov, 1 Dec) 
Final Presentations 
Some material was borrowed (ok, "stolen", but with permission) from C Guestrin, CMU  10708F06