Lecture 18: Transmitting Information Reliably over a Noisy Channel & Shannon’s Noisy Coding Theorem

Dersler20 ders

• ✓1Lecture 1: Pigeonhole Principle
• ✓2Lecture 2: Independence and Conditioning
• ✓3Lecture 3: Inclusion-Exclusion
• ✓4Lecture 4: Counting
• ✓5Lecture 5: More Counting and Generating Functions
• ✓6Lecture 6: More on Generating Functions
• ✓7Lecture 7: Generating Functions for Catalan Numbers
• ✓8Lecture 8: Tail Bounds
• ✓9Lecture 9: Chernoff Bounds
• ✓10Lecture 10: Modular Arithmetic
• ✓11Lecture 11: Basic Group Theory
• ✓12Lecture 12: Introduction to Linear Programming
• ✓13Lecture 13: Duality in Linear Programming
• ✓14Lecture 14: Zero-Sum Games
• ✓15Lecture 15: Max-Flow Min-Cut Theorem
• ✓16Lecture 16: Data Compression and Shannon’s Noiseless Coding Theorem
• ✓17Lecture 17: Huffman Coding
• ✓18Lecture 18: Transmitting Information Reliably over a Noisy Channel & Shannon’s Noisy Coding Theorem
• ✓19Lecture 19: Error-Correcting Codes—Hamming Codes
• ✓20Lecture 20: Reed-Solomon Codes

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