Here is a good online course on Information
Theory, hosted at Utah
State University.
This course "explores the fundamental limits of the representation and
transmission of information. We will focus on the definition and
implications of (information) entropy, the source coding theorem, and
the channel coding theorem. These concepts provide a vital background
for researchers in the areas of data compression, signal processing,
controls, and pattern recognition." The class notes and examples, by
topic, are linked below. This course can also be downloaded in zip
format (7.5mb).
Information
Theory Class Topics:
| No. |
Topic |
|
|
| 1 |
Introduction to Information Theory |
HTML |
PDF |
| 2 |
Definitions and Basic Facts |
HTML |
PDF |
| 3 |
Some More Bounds |
HTML |
PDF |
| 4 |
Application of Information Theory
to Blind Source Separation |
HTML |
PDF |
| 5 |
The Asymptotic Equipartition
Property |
HTML |
PDF |
| 6 |
Entropy Rates |
HTML |
PDF |
| 7 |
Data Compression |
HTML |
PDF |
| 8 |
Arithmetic Coding |
HTML |
PDF |
| 9 |
Channel Capacity |
HTML |
PDF |
| 10 |
More on Channel Capacity |
HTML |
PDF |
| 11 |
Differential Entropy |
HTML |
PDF |
| 12 |
The Gaussian Channel |
HTML |
PDF |
| 13 |
Bits and Queues |
HTML |
PDF |
| 14 |
Maximum Entropy Estimation |
HTML |
PDF |
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