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Lecture 2: Entropy and Data Compression (I): Introduction to Compression, Information Theory and Entropy

Published on Nov 05, 201236504 Views

David MacKay

Course on Information Theory, Pattern Recognition, and Neural Networks

Related categories

Bayesian LearningNeural NetworksPattern RecognitionInformation Theory

Chapter list

Data Compression: How To Measure Information Content 00:00
A Noisy Channel (1)00:22
The Binary Symmetric Channel00:30
Shannon's Noisy Channel Coding Theorem (1)01:08
Predict The Missing Text (1)02:48
Predict The Missing Text (2)04:50
A Noisy Channel (2)05:00
A Noisy Channel (3)05:52
A Bent Coin (1)07:11
A Bent Coin (2)07:18
An Ensemble (1)09:06
An Ensemble (2)10:19
Shannon Information Content (1)11:06
Shannon Information Content (2)12:30
Shannon Information Content (3)14:23
Shannon Information Content (4)15:32
Shannon Information Content (5)17:18
Entropy (1)19:09
The Weighing Problem (1)20:42
The Weighing Problem (2)21:12
The Weighing Problem (3)22:50
The Weighing Problem (4)24:19
The Weighing Problem (5)24:58
The Weighing Problem (6)25:12
The Weighing Problem (7)27:08
The Weighing Problem (8)28:37
The Weighing Problem (10)29:53
The Weighing Problem (11)37:03
Entropy Maximization (1)38:35
Entropy Maximization (2)39:25
Entropy Maximization (3)40:16
The Bent Coin Lottery46:46
Homework49:36