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Lecture 11: Approximating Probability Distributions (I): Clustering As An Example Inference Problem

Published on Nov 05, 201213506 Views

David MacKay

Course on Information Theory, Pattern Recognition, and Neural Networks

Related categories

Bayesian LearningNeural NetworksPattern RecognitionInformation Theory

Chapter list

Clustering (1)00:00
Course Outline00:06
The Course Website & Book00:23
Monte Carlo Methods (1)00:34
Upcoming Lectures00:46
Inferring Parameters In Science Experiment (1)01:18
Inferring Parameters In Science Experiment (2)01:55
Question: Channel With Gaussian Noise (1)02:36
Question: Channel With Gaussian Noise (2)02:51
Question: Channel With Gaussian Noise (3)04:06
Question: Channel With Gaussian Noise (4)05:00
Question: Channel With Gaussian Noise (5)06:16
Question: Channel With Gaussian Noise (6)06:52
Mixture Of Gaussians (1)08:04
Clustering (2)09:18
Clustering (3)10:16
Clustering (4)11:30
K Means Clustering (1)12:26
K Means Clustering (2)13:15
K Means Clustering (3)14:50
K Means Clustering (4)15:22
K Means Clustering (5)16:30
K Means Clustering (6)16:51
K Means Clustering (7)17:38
K Means Clustering (8)18:34
K Means Clustering (9)20:13
K Means Clustering (10)21:54
K Means Clustering (11)23:52
K Means Clustering (12)25:29
K Means Clustering (13)26:01
K Spherical Gaussians (1)26:37
K Spherical Gaussians (3)31:03
K Spherical Gaussians (4)33:24
K Spherical Gaussians (5)34:23
K Spherical Gaussians (6)38:52
K Spherical Gaussians (7)39:28
K Spherical Gaussians (2)40:24
K Spherical Gaussians (8)41:53
Soft K Means (1)42:42
Soft K Means (2)43:10
Soft K Means (4)46:01
Soft K Means (5)46:09
Soft K Means (6)47:03
Soft K Means (7)47:54
Soft K Means (3)49:39