Course Name: CS6810 : Information Theory and Coding

Description: Probability and Random Processes: Axioms of probability? random variables? statistical averages. Information Theory Basics: Definition: uncertainty, information, entropy? relation between uncertainty, information and entropy. Source-Coding: Shannon?s source coding theorem? Uniquely decodable codes? instantaneous codes? Kraft inequality? McMillan?s inequality, Lempel-Ziv algorithm for source coding? run-length coding? design of optimal quantiser. Optimal Codes: Optimality? Huffman codes, r-ary Huffman codes, Shannon-Fano codes? entropy and optimal codes. Information Channels: Shannon?s channel coding theorem? redundancy? binary symmetric channel? system entropy? mutual information? Shannon?s channel capacity theorem? channel capacity and coding Error-Control coding: discrete-memoryless channels? error detection and error correction codes? linear block codes: Hamming codes, optimal linear codes? basic finite field theory? Reed-Solomon codes? Berlekamp Massery Algorithm? cyclic codes: Golay, CRC? convolution

Slot: S

RoomNo:

Instructor: Hema A Murthy

Period: JUL-NOV 2013

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