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Lecture
Information Theory: Prefix-Free Codes
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Related lectures (28)
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Compression: Prefix-Free Codes
Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.
Source Coding Theorem
Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.
Lecture: Shannon
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
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.
Data Compression and Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.
Entropy and Algorithms: Applications in Sorting and Weighing
Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.
Conditional Entropy and Data Compression Techniques
Discusses conditional entropy and its role in data compression techniques.
Mutual Information and Entropy
Explores mutual information and entropy calculation between random variables.
Variational Formulation: Information Measures
Explores variational formulation for measuring information content and divergence between probability distributions.
