Lecture 17: Huffman Coding - Uedu Open

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Lecture 17: Huffman Coding

18.200 - Principles of Discrete Applied Mathematics

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Other videos (20)

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

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Principles of Discrete Applied Mathematics

Course videos (20)

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