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Lecture
Data Compression: Entropy Definition
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Entropy and Compression I
Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.
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.
Conditional Entropy and Information Theory Concepts
Discusses conditional entropy and its role in information theory and data compression.
Conditional Entropy and Data Compression Techniques
Discusses conditional entropy and its role in data compression techniques.
Data Compression and Shannon's Theorem: Entropy Calculation Example
Demonstrates the calculation of entropy for a specific example, resulting in an entropy value of 2.69.
Information Theory: Review and Mutual Information
Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.
Compression: Prefix-Free Codes
Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.
Data Compression and Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.
Information Measures: Entropy and Information Theory
Explains how entropy measures uncertainty in a system based on possible outcomes.
Entropy and Algorithms: Applications in Sorting and Weighing
Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.
