Location: EU Supported » PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning » Machine Learning Summer School 2009 - Cambridge

Graphical Models

author:Zoubin Ghahramani, University of Cambridge
published: Nov. 2, 2009,   recorded: August 2009,   views: 936
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Slides

Slides
0:00 Machine Learning Summer School
0:22 Three main kinds of graphical models
1:45 Why do we need graphical models?
3:38 Conditional Independence
5:29 Conditional and Marginal Independence (Examples)
10:00 Factor Graphs - 1
12:10 Factor Graphs - 2
14:32 Proving Conditional Independence
18:09 Undirected Graphical Models - 1
20:17 Undirected Graphical Models - 2
21:47 Comparing Undirected Graphs and Factor Graphs
25:27 Problems with Undirected Graphs and Factor Graphs
28:19 Directed Acyclic Graphical Models (Bayesian Networks) - 1
30:33 Directed Acyclic Graphical Models (Bayesian Networks) - 2
35:40 Examples of D-Separation in DAGs
37:22 From Directed Trees to Undirected Trees
40:21 Directed Graphs for Statistical Models: Plate Notation
41:43 Expressive Power of Directed and Undirected Graphs
43:41 Summary - 1
44:37 Propagation in Factor Graphs
44:54 Elimination Rules for Factor Graphs - 1
46:34 Elimination Rules for Factor Graphs - 2
52:03 Inference in Hidden markov models and Linear Gaussian state-space models
54:03 Inference in multiply connected DAGs
55:13 The Junction Tree Algorithm 1
55:32 The Junction Tree Algorithm 2
56:36 The Junction Tree Algorithm 3
59:35 The Junction Tree Algorithm 4
59:45 The Junction Tree Algorithm 5
62:16 Summary - 2

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Reviews and comments:

Comment1 Gregor Gorjanc, November 6, 2009 at 6:24 p.m.:

Video is missing for the part right after the short break - the slides 20-33.

Comment2 chu, December 11, 2009 at 4:20 p.m.:

The Inference and Propagation Algorithms part is missing.

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