Information Measures: Entropy and Information Theory
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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Covers information measures like entropy, Kullback-Leibler divergence, and data processing inequality, along with probability kernels and mutual information.