{"product_id":"information-theory-from-coding-to-learning-hardback-9781108832908","title":"Information Theory; From Coding to Learning (Hardback) 9781108832908","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eInformation Theory\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eFrom Coding to Learning\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eAn enthusiastic introduction to the fundamentals of information theory, from classical Shannon theory to modern statistical learning.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eYury Polyanskiy (Author), Yihong Wu (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781108832908, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 2 January 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e748 pages\u003cbr\u003e26.1 x 18.2 x 4.3 cm, 1.78 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'The book is excellent. One of the landmark texts on IT.' Masoud Salehi, Northeastern University\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePart I. Information measures: 1. Entropy\u003cbr\u003e 2. Divergence\u003cbr\u003e 3. Mutual information\u003cbr\u003e 4. Variational characterizations and continuity of information measures\u003cbr\u003e 5. Extremization of mutual information: capacity saddle point\u003cbr\u003e 6. Tensorization and information rates\u003cbr\u003e 7. f-divergences\u003cbr\u003e 8. Entropy method in combinatorics and geometry\u003cbr\u003e 9. Random number generators\u003cbr\u003e Part II. Lossless Data Compression: 10. Variable-length compression\u003cbr\u003e 11. Fixed-length compression and Slepian-Wolf theorem\u003cbr\u003e 12. Entropy of ergodic processes\u003cbr\u003e 13. Universal compression\u003cbr\u003e Part III. Hypothesis Testing and Large Deviations: 14. Neyman-Pearson lemma\u003cbr\u003e 15. Information projection and large deviations\u003cbr\u003e 16. Hypothesis testing: error exponents\u003cbr\u003e Part IV. Channel Coding: 17. Error correcting codes\u003cbr\u003e 18. Random and maximal coding\u003cbr\u003e 19. Channel capacity\u003cbr\u003e 20. Channels with input constraints. Gaussian channels\u003cbr\u003e 21. Capacity per unit cost\u003cbr\u003e 22. Strong converse. Channel dispersion. Error exponents. Finite blocklength\u003cbr\u003e 23. Channel coding with feedback\u003cbr\u003e Part V. Rate-distortion Theory and Metric Entropy: 24. Rate-distortion theory\u003cbr\u003e 25. Rate distortion: achievability bounds\u003cbr\u003e 26. Evaluating rate-distortion function. Lossy Source-Channel separation\u003cbr\u003e 27. Metric entropy\u003cbr\u003e Part VI. : 28. Basics of statistical decision theory, 29. Classical large-sample asymptotics\u003cbr\u003e 30. Mutual information method\u003cbr\u003e 31. Lower bounds via reduction to hypothesis testing, 32. Entropic bounds for statistical estimation\u003cbr\u003e 33. Strong data processing inequality.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Communications engineering \/ telecommunications [\u003ca title=\"See our other books on Communications engineering \/ telecommunications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Communications%20engineering%20\/%20telecommunications%20%5BTJK%5D%22\"\u003eTJK\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Brand New","offer_id":52460726124824,"sku":"9781108832908","price":58.55,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781108832908i.jpg?v=1785459373","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/information-theory-from-coding-to-learning-hardback-9781108832908","provider":"Freshly Printed Books","version":"1.0","type":"link"}