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Synopsis
An enthusiastic introduction to the fundamentals of information theory, from classical Shannon theory to modern statistical learning.
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About the Authors
Yury Polyanskiy is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, with a focus on information theory, statistical machine learning, error-correcting codes, wireless communication, and fault tolerance. He is the recipient of the 2020 IEEE Information Theory Society James Massey Award for outstanding achievement in research and teaching in Information Theory.
Yihong Wu is a Professor of Statistics and Data Science at Yale University, focusing on the theoretical and algorithmic aspects of high-dimensional statistics, information theory, and optimization. He is the recipient of the 2018 Sloan Research Fellowship in Mathematics.
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Information Theory: From Coding to Learning
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Information Theory: From Coding to Learning
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ISBN 10: 1108832903
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Information Theory: From Coding to Learning
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Information Theory : From Coding to Learning
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Information Theory: From Coding to Learning
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Information Theory: From Coding to Learning
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