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Lecture
Source Coding Theorem: Fundamentals and Models
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Related lectures (30)
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Information Measures: Entropy and Information Theory
Explains how entropy measures uncertainty in a system based on possible outcomes.
Source Coding Theorems: Entropy and Source Models
Covers source coding theorems, entropy, and various source models in information theory.
Lecture: Shannon
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Information Theory Basics
Introduces information theory basics, including entropy, independence, and binary entropy function.
Conditional Entropy and Information Theory Concepts
Discusses conditional entropy and its role in information theory and data compression.
Variational Formulation: Information Measures
Explores variational formulation for measuring information content and divergence between probability distributions.
Source Coding Theorem
Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.
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 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.
Data Compression and Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.
