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Lecture
Information Theory and Coding
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Related lectures (30)
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Information Theory and Coding
Covers source coding, Kraft's inequality, mutual information, Huffman procedure, and properties of tropical sequences.
Lecture: Shannon
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Source Coding and Prefix-Free Codes
Covers source coding, injective codes, prefix-free codes, and Kraft's inequality.
Information Theory: Source Coding, Cryptography, Channel Coding
Covers source coding, cryptography, and channel coding in communication systems, exploring entropy, codes, error channels, and future related courses.
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: Entropy and Information Processing
Explores entropy in information theory and its role in data processing and probability distributions.
Entropy and Data Compression: Huffman Coding Techniques
Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.
Information Measures: Entropy and Information Theory
Explains how entropy measures uncertainty in a system based on possible outcomes.
Information Theory Basics
Introduces information theory basics, including entropy, independence, and binary entropy function.
