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Machine learning for cognitive science 3: Kernel methods and Bayesian methods
Published on Jun 15, 20105892 Views
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Chapter list
Introduction to Graphical Models00:00
A Typical Bound for Pattern Recognition00:00
Motivation - 100:24
Motivation - 201:24
SRM02:37
Finding a Good Function Class02:39
Bayes Rule and Independece03:26
Kernels and Feature Spaces03:47
Basic Graph Definitions - 104:42
Example: All Degree 2 Monomials05:47
Basic Graph Definitions - 206:56
Belief Networks (Bayesian Networks)07:18
General Product Feature Space07:37
Example Part 1 - 108:03
The Kernel Trick - 209:47
Example Part 1 - 210:08
Example Part 1 - 310:32
Example Part 1 - 410:43
Example- Part 2: Specifying the Tables11:01
The Kernel Trick - 111:29
Mercer's Theorem11:43
Example Part 3: Inference12:01
Independence ╨ in Belief Networks - Part 112:55
The Mercer Feature Map13:03
The Kernel Trick - 313:04
Positive Definite Kernels13:06
Independence ╨ in Belief Networks - Part 216:09
Collider - 116:32
Collider - 217:13
Elementary Properties of PD Kernels18:58
General Rule for Independence in Belief Networks19:56
Example of using teh Independence Rule for Time - Series Modeling - 120:19
Example of using teh Independence Rule for Time - Series Modeling - 222:16
Example of using teh Independence Rule for Time - Series Modeling - 323:02
Markov Network25:10
Example Application of MArkov Network - 126:20
Example Application of MArkov Network - 228:41
Independence ╨ in Markov Networks29:59
General Rule for Independence in Markov Networks31:39
Alternative Rule for Independence in Belief Networks - 132:20
Alternative Rule for Independence in Belief Networks - 232:39
Alternative Rule for Independence in Belief Networks - 332:40
Expressiveness of Belief and Markov Networks33:12
The Feature Space for PD Kernels35:00
Factor Graphs35:48
Inference - 136:56
Inference - 238:16
Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 138:36
Turn it Into a Linear Space39:24
Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 240:16
Endow it With a Dot Product - 140:40
Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 340:52
Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 441:27
Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 541:38
Sum- Product Algorithm for Factor Graphs - Non Branching Tree - 642:08
Sum - Procuct Algorithm for Factor Graphs42:37
Inference in Hidden Markov Models (HMM) - Part 144:45
The Reproducing Kernel Property44:45
Endow it With a Dot Product - 246:32
Inference in Hidden Markov Models - Part 246:54
Localisation Example - Part 147:46
Localisation Example - Part 249:20
Localisation Example - Part 349:59
Natural Language Model Example - Part 152:32
An Example of a Kernel Algorithm - 154:08
Natural Language Model Example - Part 254:10
Learning54:58
Summarising the Parameter Posterior57:30
Naive Bayes Classifier58:02
An Example of a Kernel Algorithm - 258:23
Naive Bayes: Learning59:31
Naive Bayes: Maximum Likelihood01:00:22
Naive Bayes: Bayesian Approach01:02:07
Learning in Markov Networks: Maximum Likelihood01:02:25
Learning Parameters with Hidden Variables01:03:20
Expectation Maximisation Algorithm for Maximum Likelihood01:04:58
Reading01:08:05