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Lecture
Information Theory: Source Coding & Channel Coding
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Related lectures (27)
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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 Data Compression Techniques
Discusses conditional entropy and its role in data compression techniques.
Source Coding Theorems: Entropy and Source Models
Covers source coding theorems, entropy, and various source models in information theory.
Entropy and Algorithms: Applications in Sorting and Weighing
Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.
Compression: Prefix-Free Codes
Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.
Conditional Entropy and Information Theory Concepts
Discusses conditional entropy and its role in information theory and data compression.
Compression: Kraft Inequality
Explains compression and Kraft inequality in codes and sequences.
Information Theory and Coding
Covers source coding, Kraft's inequality, mutual information, Huffman procedure, and properties of tropical sequences.
Stochastic Processes: Sequences and Compression
Explores compression in stochastic processes through injective codes and prefix-free codes.
Source Coding and Prefix-Free Codes
Covers source coding, injective codes, prefix-free codes, and Kraft's inequality.
