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Information Theory From Coding to Learning

Authors

Yury Polyanskiy
, Massachusetts Institute of Technology,
Yihong Wu
, Yale University, Connecticut
Published 2025

Description

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…

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Key features

  • Provides a systematic treatment of information-theoretic techniques in statistical learning and high-dimensional statistics
  • Develops information theory for both continuous and discrete variables providing examples relevant to statistical and machine learning applications
  • Focuses on finite block length (non-asymptotic) results, equipping students with information theory knowledge required for modern applications such as 6G and future network design
  • Advanced material suitable for skipping on first reading is clearly indicated, enabling a fast introduction to fundamental concepts which can be enhanced with additional material on re-reading

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