0.25
0.5
0.75
1.25
1.5
1.75
2
Lecture 11: Approximating Probability Distributions (I): Clustering As An Example Inference Problem
Published on Nov 05, 201213455 Views
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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