Cognitive Science and Machine Learning Summer School (MLSS), Sardinia 2010

Introduction to Graphical Models

author: Silvia Chiappa, Faculty of Mathematics, University of Cambridge
published: Aug. 13, 2010, recorded: May 2010, views: 1678

Slides

Slides
0:00	Introduction to Graphical Models
0:24	Motivation - 1
1:24	Motivation - 2
3:26	Bayes Rule and Independece
4:42	Basic Graph Definitions - 1
6:56	Basic Graph Definitions - 2
7:18	Belief Networks (Bayesian Networks)
8:03	Example Part 1 - 1
10:08	Example Part 1 - 2
10:32	Example Part 1 - 3
10:43	Example Part 1 - 4
11:01	Example- Part 2: Specifying the Tables
12:01	Example Part 3: Inference
12:55	Independence ╨ in Belief Networks - Part 1
16:09	Independence ╨ in Belief Networks - Part 2
16:32	Collider - 1
17:13	Collider - 2
19:56	General Rule for Independence in Belief Networks
20:19	Example of using teh Independence Rule for Time - Series Modeling - 1
22:16	Example of using teh Independence Rule for Time - Series Modeling - 2
23:02	Example of using teh Independence Rule for Time - Series Modeling - 3
25:10	Markov Network
26:20	Example Application of MArkov Network - 1
28:41	Example Application of MArkov Network - 2
29:59	Independence ╨ in Markov Networks
31:39	General Rule for Independence in Markov Networks
32:20	Alternative Rule for Independence in Belief Networks - 1
32:39	Alternative Rule for Independence in Belief Networks - 2
32:40	Alternative Rule for Independence in Belief Networks - 3
33:12	Expressiveness of Belief and Markov Networks
35:48	Factor Graphs
36:56	Inference - 1
38:16	Inference - 2
38:36	Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 1
40:16	Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 2
40:52	Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 3
41:27	Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 4
41:38	Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 5
42:08	Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 6
42:37	Sum - Procuct Algorithm for Factor Graphs
44:23	Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 6
44:31	Sum - Procuct Algorithm for Factor Graphs
44:45	Inference in Hidden Markov Models (HMM) - Part 1
46:54	Inference in Hidden Markov Models - Part 2
47:46	Localisation Example - Part 1
49:20	Localisation Example - Part 2
49:59	Localisation Example - Part 3
52:32	Natural Language Model Example - Part 1
54:10	Natural Language Model Example - Part 2
54:58	Learning
57:30	Summarising the Parameter Posterior
58:02	Naive Bayes Classifier
59:31	Naive Bayes: Learning
60:22	Naive Bayes: Maximum Likelihood
62:07	Naive Bayes: Bayesian Approach
62:25	Learning in Markov Networks: Maximum Likelihood
63:20	Learning Parameters with Hidden Variables
64:58	Expectation Maximisation Algorithm for Maximum Likelihood
68:05	Reading

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