Information Theory: From Coding to Learning - Tapa dura

Polyanskiy, Yury; Wu, Yihong

 
9781108832908: Information Theory: From Coding to Learning
Tapa dura
ISBN 10: 1108832903 ISBN 13: 9781108832908
Editorial: Cambridge University Press, 2024

Sinopsis

This enthusiastic introduction to the fundamentals of information theory builds from classical Shannon theory through to modern applications in statistical learning, equipping students with a uniquely well-rounded and rigorous foundation for further study. Introduces core topics such as data compression, channel coding, and rate-distortion theory using a unique finite block-length approach. With over 210 end-of-part exercises and numerous examples, students are introduced to contemporary applications in statistics, machine learning and modern communication theory. This textbook presents information-theoretic methods with applications in statistical learning and computer science, such as f-divergences, PAC Bayes and variational principle, Kolmogorov's metric entropy, strong data processing inequalities, and entropic upper bounds for statistical estimation. Accompanied by a solutions manual for instructors, and additional standalone chapters on more specialized topics in information theory, this is the ideal introductory textbook for senior undergraduate and graduate students in electrical engineering, statistics, and computer science.

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Acerca de los autores

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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Editorial
Cambridge University Press
Año de publicación
2024
Idioma
Inglés
ISBN 10 
1108832903
ISBN 13 
9781108832908
Encuadernación
Tapa dura
Número de páginas
748
Contacto del fabricante
Cambridge University Press
https://www.cambridge.org/

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