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Information Theory, Inference and Learning Algorithms Illustrated Edition

by David J. C. MacKay (Author)
4.6 4.6 out of 5 stars 165 ratings
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Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.
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  1. ISBN-10
    0521642981
  2. ISBN-13
    978-0521642989
  3. Edition
    Illustrated
  4. Publisher
    Cambridge University Press
  5. Publication date
    October 6, 2003
  6. Language
    English
  7. Dimensions
    1.75 x 1.25 x 10 inches
  8. Print length
    640 pages
  9. See all details
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Editorial Reviews

Review

"...a valuable reference...enjoyable and highly useful."
American Scientist


"...an impressive book, intended as a class text on the subject of the title but having the character and robustness of a focused encyclopedia. The presentation is finely detailed, well documented, and stocked with artistic flourishes."
Mathematical Reviews


"Essential reading for students of electrical engineering and computer science; also a great heads-up for mathematics students concerning the subtlety of many commonsense questions."
Choice


"An utterly original book that shows the connections between such disparate fields as information theory and coding, inference, and statistical physics."
Dave Forney, Massachusetts Institute of Technology


"This is an extraordinary and important book, generous with insight and rich with detail in statistics, information theory, and probabilistic modeling across a wide swathe of standard, creatively original, and delightfully quirky topics. David MacKay is an uncompromisingly lucid thinker, from whom students, faculty and practitioners all can learn."
Peter Dayan and Zoubin Ghahramani, Gatsby Computational Neuroscience Unit, University College, London


"An instant classic, covering everything from Shannon's fundamental theorems to the postmodern theory of LDPC codes. You'll want two copies of this astonishing book, one for the office and one for the fireside at home."
Bob McEliece, California Institute of Technology


"An excellent textbook in the areas of infomation theory, Bayesian inference and learning alorithms. Undergraduate and post-graduate students will find it extremely useful for gaining insight into these topics."
REDNOVA


"Most of the theories are accompanied by motivations, and explanations with the corresponding examples...the book achieves its goal of being a good textbook on information theory."
ACM SIGACT News

Book Description

Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.

Product details

  • Publisher ‏ : ‎ Cambridge University Press; Illustrated edition (October 6, 2003)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 640 pages
  • ISBN-10 ‏ : ‎ 0521642981
  • ISBN-13 ‏ : ‎ 978-0521642989
  • Item Weight ‏ : ‎ 3.36 pounds
  • Dimensions ‏ : ‎ 1.75 x 1.25 x 10 inches
  • Customer Reviews:
    4.6 4.6 out of 5 stars 165 ratings

About the authors

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  1. David J. C. MacKay

    David J. C. MacKay

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    David MacKay is a professor in the Department of Physics at Cambridge University, a Fellow of the Royal Society, and Chief Scientific Advisor to the Department of Energy and Climate Change, UK.

    See more on the author's page
  2. David Mackay

    David Mackay

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Customer reviews

4.6 out of 5 stars
4.6 out of 5
165 global ratings
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The cover of the book is upside down compared to the inside. I didn't open the book until the returning window closes. It doesn't affect reading but it's visually annoying.
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Top reviews from the United States

Christ
5.0 out of 5 stars The most self-contained machine learning book there
Reviewed in the United States on February 20, 2021
Verified Purchase
Read the book is like talking to a teacher. I can feel the soul of the author. (He had passed away). The book contains solutions to selected problems that are convenient to me for self-study.
2 people found this helpful
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Summer
5.0 out of 5 stars Like this book
Reviewed in the United States on December 15, 2020
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Highly recommended. Very coherent and readable. Unique angle of view. The author didn't try to scare the reader away like a lot of other authors did.
One person found this helpful
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david
5.0 out of 5 stars A great supplement to the subject
Reviewed in the United States on September 15, 2011
Verified Purchase
This is a really good book. It serves as a good introduction to Information theory but it has enough depth and cover enough material be to interesting and insightful even to someone who has already studies the subject in depth. This book is fairly high level and though I found it very interesting and insightful it does not have enough practical information to be useful (on its own) for solving problems in information theory or writing learning algorithms.
5 people found this helpful
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Ying Zhang
5.0 out of 5 stars Finally a great hardcover
Reviewed in the United States on November 17, 2020
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The hardcover is much better than the soft cover. Mackay was a visionary, can't wait to read the book.
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Jose Ortiz
4.0 out of 5 stars Good multidisciplinary connections, explanations often lacking however
Reviewed in the United States on October 3, 2018
Verified Purchase
As a grad student in optimization with a background in physics, I really enjoy the multi-disciplinary approach of this book. Connections between different fields are frequent throughout the book. However, I often am frustrated with the book's style. Often, something that needs further explanation or clarification does not receive it, and I am forced to "google" the explanation that should be there but isn't.
7 people found this helpful
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K. Josic
5.0 out of 5 stars one of the best technical books out there
Reviewed in the United States on May 27, 2011
Verified Purchase
Other reviewers have provided all the details you need to know before buying.
Just to chime in that this is one of the best technical books I have ever read.
It brims with insight and beautiful illustrations of ideas both old and novel.
Although you can find a free copy online, do consider getting the print version.
It is a great tome to have, and Dr. MacKay certainly deserves the royalties.
10 people found this helpful
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Chi-ken Lu
5.0 out of 5 stars I recommend it to people who have good physics sense and ...
Reviewed in the United States on November 15, 2017
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MacKay is the pioneer in the field of machine learning theory. I recommend it to people who have good physics sense and want to learn the basic idea of learning theory.
5 people found this helpful
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M. ZENG
5.0 out of 5 stars It is fun to read this book
Reviewed in the United States on July 3, 2013
Verified Purchase
First of all, the shipping is fast and the price is low. It is a new book but the price is lower than the used one. Second, the book itself is worth reading for fun. It combines so many interesting topics in an unified framework: Bayesian framework, from information theory to neuro network.
2 people found this helpful
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Top reviews from other countries

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mhadi shateri
5.0 out of 5 stars Very Nice Book to read
Reviewed in Canada on July 16, 2021
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It is a great book to read and enjoy
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Amazon Customer
5.0 out of 5 stars It's a good book about information theory and basics of neural nets and Bayesian statististics
Reviewed in India on February 24, 2023
Verified Purchase
The book is good but you should know inferential statistics beforehand and multivariate calculus to read this. It's theory heavy which is a good thing.
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Dani
5.0 out of 5 stars Muy bueno
Reviewed in Spain on November 12, 2020
Verified Purchase
El libro está un poco desordenado y sería imposible de seguir si no fuese por un índice de dependencias que tiene al principio y que te indica que capítulos dependen de otros. Pero a parte de eso, el libro es genial. Está muy bien explicado y muestra conexiones entre diferentes campos que no es habitual encontrar. Hay unos videos del autor en Youtube, que explican parte del libro. Muy interesante verlos libro en mano.
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hk2018
5.0 out of 5 stars 値段を超える充実した内容
Reviewed in Japan on August 2, 2018
Verified Purchase
大変素晴らしい本です。既に pdf 版（無料）が公開されているので内容は知っていましたが、本体（冊子体）を見ると実に充実した内容がコンパクトにかつ読みやすくまとまっています。本書では、まず情報理論の基礎を詳しく説明し、その応用として Neural Networks など最近の話題に関し適切な解説があります。情報理論と機械学習は本来、関連が非常に強いはずですが、この点に関してきちんと扱った本は、私は他に知りません。本書の値段は安いとは言えませんが、それを遥かに超える価値があります。私のお勧めの使い方としては、本体（冊子体）を手元に置き、pdf 版でのフリーテキスト検索と組み合わせると、鬼に金棒です。是非、お試しあれ。
5 people found this helpful
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Tavanez
5.0 out of 5 stars La bibbia dell'ECC
Reviewed in Italy on March 3, 2016
Verified Purchase
Pochi pensano ai problemi che pone comunicare attraverso un mezzo affetto da rumore. Molti non sospettano neppure cio' costituisca un problema. Eppure le missioni interplanetarie o i CD non esisterebbero senza una teoria a riguardo. Il controllo di parita' come mezzo per certificare la correttezza di un messaggio binario e' una tecnica nota a molti, ma quando si tratta di capire quale ridondanza debba essere aggiunta ad un messaggio per garantirne la corretta ricostruzione a valle di una linea affetta da un livello quantificabile di rumore, il numero cala drasticamente. Questo e' un libro sicuramente destinato a chi per lavoro debba occuparsi seriamente di Error Correction Code, ma e' anche un libro che qualsiasi curioso di materie scientifiche dovrebbe leggere, anche se non si tratta certo di un testo divulgativo nel quale si evita accuratamente il ricorso alle formule. Al contrario, ogni capitolo e' corredato da esercizi e il testo e' certamente impegnativo. Ma questo rende la lettura stimolante.
4 people found this helpful
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