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Lecture
Information Theory: Source Coding
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Related lectures (29)
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Information Theory and Coding
Covers source coding, Kraft's inequality, mutual information, Huffman procedure, and properties of tropical sequences.
Source Coding and Prefix-Free Codes
Covers source coding, injective codes, prefix-free codes, and Kraft's inequality.
Information Theory and Coding: Source Coding
Covers source coding, encoder design, and error probability analysis in information theory and coding.
Lecture: Shannon
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Information Theory: Source Coding & Channel Coding
Covers the fundamentals of information theory, focusing on source coding and channel coding.
Random Coding: Achievability and Proof Variants
Explores random coding achievability and proof variants in information theory, emphasizing achievable rates and architectural principles.
Advanced Information Theory: Random Binning
Explores random binning in advanced information theory, focusing on assigning labels based on typicality and achieving negligible error rates in source coding.
Compression: Prefix-Free Codes
Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.
Information Theory: Channel Capacity and Convex Functions
Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.
Information Theory and Coding
Covers expected code word length, Huffman procedure, and entropy in coding theory.
