| 1 |
Introduction, Review of Random Variables, Entropy, Mutual Information, Chain Rules (PDF) |
| 2 |
Jensen's Inequality, Data Processing Theorem, Fanos's Inequality (PDF) |
| 3 |
Markov Chain, Entropy Rate of Random Processes (PDF) |
| 4 |
Different Types of Convergence, Asymptotic Equipartition Property (AEP), Typical Set, Joint Typicality (PDF) |
| 5 |
Data Compression, Kraft Inequality, Optimal Codes (PDF) |
| 6 |
Huffman Codes, Sensitivity of Distribution, Elias Code (PDF) |
| 7 |
Gambling (PDF) |
| 8 |
Channel Capacity, Symmetric and Erasure Channels (PDF) |
| 9 |
Coding Theorem (PDF) |
| 10 |
Strong Coding Theorem (PDF) |
| 11 |
Strong Coding Theorem (cont.) (PDF) |
| 12 |
Feedback Capacity (PDF) |
| 13 |
Joint Source Channel Coding (PDF) |
| 14 |
Differential Entropy (PDF) |
|
Recitation: Background Materials Review (PDF) |
| 15 |
Gaussian Channel (PDF) |
| 16 |
Gaussian Channels: Parallel, Colored Noise, Inter-symbol Interference (PDF) |
| 17 |
Maximizing Entropy (PDF) |
| 18 |
Gaussian Channels with Feedback (PDF) |
| 19 |
Fading Channels (PDF) |
| 20 |
Types, Universal Source Coding, Sanov's Theorem (PDF) |
| 21 |
Multiple Access Channels (PDF) |
| 22 |
Slepian-Wolf Coding (PDF) |
| 23 |
Broadcast Channels (PDF) |
| 24 |
Channel Side Information, Wide-band Channels (PDF) |