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.

An enthusiastic introduction to the fundamentals of information theory, from classical Shannon theory to modern statistical learning.

Table of Contents

Part I Information measures

1-Entropy

2-Divergence

3-Mutual information

4-Variational characterizations and continuity of information measures

5-Extremization of mutual information: capacity saddle point

6-Tensorization and information rates

7-f-divergences

8-Entropy method in combinatorics and geometry

9-Random number generators

Exercises for Part I

Part II Lossless data compression

10-Variable-length compression

11-Fixed-length compression and Slepian-Wolf theorem

12-Entropy of ergodic processes

13-Universal compression

Exercises for Part II

Part III Hypothesis testing and large deviations

14-Neyman-Pearson lemma

15-Information projection and large deviations

16-Hypothesis testing: error exponents

Exercises for Part III

Part V Rate-distortion theory and metric entropy

17-Rate-distortion theory

18-Rate distortion: achievability bounds

19-Evaluating rate-distortion function. Lossy Source-Channel separation.

20-Metric entropy

Exercises for Part V

Part VI Statistical applications

21-Basics of statistical decision theory

22-Classical large-sample asymptotics

23-Mutual information method

24-Lower bounds via reduction to hypothesis testing

25-Entropic bounds for statistical estimation

26-Strong data processing inequality

Exercises for Part VI

About the Author

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.