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Lecture Notes



Lecture Notes

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

 








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