Information Measures: Part 2

In course

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Description

This lecture covers information measures, including entropy, joint entropy, conditional entropy, mutual information, and Kullback-Leibler divergence. It explains the concepts using mathematical formulas and examples, emphasizing the importance of these measures in information theory and data processing applications.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_hwejeze0

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Ontological neighbourhood

Mathematics

Algebra: Linear algebra, Abstract algebra

Information engineering

Information theory: Channel capacity

Computer engineering

Computer architecture: Topics in computer architecture

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