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Description
This lecture covers the concept of entropy in information theory, going back to Claude Shannon's work in 1948. It explains how entropy measures the uncertainty or randomness in a system based on the number of possible outcomes.
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Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.
Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.
