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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 172 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
    September 25, 2003
  6. Language
    English
  7. Dimensions
    7 x 1.45 x 10 inches
  8. Print length
    642 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 (September 25, 2003)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 642 pages
  • ISBN-10 ‏ : ‎ 0521642981
  • ISBN-13 ‏ : ‎ 978-0521642989
  • Item Weight ‏ : ‎ 3.4 pounds
  • Dimensions ‏ : ‎ 7 x 1.45 x 10 inches
  • Customer Reviews:
    4.6 4.6 out of 5 stars 172 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.

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  2. David Mackay

    David Mackay

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4.6 out of 5 stars
172 global ratings

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Customers find the book well-written and engaging. They appreciate its clear presentation and unique perspective. Readers describe the pacing as good and the book as one of the best in machine learning.

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14 customers mention "Readability"10 positive4 negative

Customers find the book well-written and interesting. They say it provides a clear exposition on the subject, with an excellent scope and scope. The book combines many interesting topics in an unified framework, providing background and motivation for the material.

"...The book contains solutions to selected problems that are convenient to me for self-study." Read more

"...Just to chime in that this is one of the best technical books I have ever read...." Read more

"...with a background in physics, I really enjoy the multi-disciplinary approach of this book...." Read more

"...Second, the book itself is worth reading for fun. It combines so many interesting topics in an unified framework: Bayesian framework, from..." Read more

4 customers mention "Pacing"4 positive0 negative

Customers find the book well-written and readable. They appreciate the unique perspective and clear presentation.

"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." Read more

"A sense of humor and a wide-ranging yet clear presentation." Read more

"Good book on topic, well written." Read more

"Distinctly great text..." Read more

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Top reviews from the United States

  • 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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  • 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
    Verified Purchase
    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.
    2 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.
    11 people found this helpful
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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.
    8 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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  • Ying Zhang
    5.0 out of 5 stars Finally a great hardcover
    Reviewed in the United States on November 17, 2020
    Verified Purchase
    The hardcover is much better than the soft cover. Mackay was a visionary, can't wait to read the book.
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  • JJA
    5.0 out of 5 stars Distinctly great text
    Reviewed in the United States on April 2, 2014
    Verified Purchase
    Coverage or detail? One may not be used to getting both. This book actually uses a detailed description of those questions "left for the reader" as a way to reinforce its pedagogy. I just love this book.
    9 people found this helpful
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Top reviews from other countries

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  • Tito Spadini
    3.0 out of 5 stars Ótimo conteúdo, mas péssima qualidade de produto.
    Reviewed in Brazil on June 10, 2024
    Verified Purchase
    O conteúdo do livro dispensa comentários detalhados. Em termos de conteúdo — e somente de conteúdo —, de fato, é uma obra de respeito na área. Mas o problema não é o conteúdo; o problema é, de modo geral, a qualidade do livro como um produto. Por isso, explicarei melhor o que me levou a avaliar o produto dessa forma e a pedir reembolso.

    Trata-se de um produto que havia sido vendido, mesmo em uma promoção rara de se ver, por R$ 324,94. Podemos dizer que, portanto, não é um livro barato. Entendo que não seja um livro pequeno, assim como entendo que seja de capa dura e que seja uma edição feita no exterior, em idioma estrangeiro (inglês), o que envolve alguns custos extras.

    Porém, justamente por ter custado tanto, eu esperava uma qualidade muito superior em todos os aspectos que dizem respeito a um material impresso.

    A capa é muito bonita e bem-acabada, mas é a única parte boa a ser mencionada. O miolo todo é impresso em papel Offset, que é um dos mais simples e baratos entre os que a indústria editorial utiliza por padrão, e nem tem uma gramatura das mais altas, além de não ter qualquer acabamento que valorize mais o material. A impressão é toda (ou quase toda) em escala de cinza. Parece um livro qualquer, sem qualquer tipo de valorização do material em parte alguma.

    Exceto pelo fato de haver uma capa dura bonita, não vejo muita diferença entre esse livro e uma versão sem-vergonha que alguma pessoa poderia simplesmente imprimir em casa, usando uma mera impressora monocromática doméstica com tinta corante de baixa qualidade sobre papel sulfite A4 de 75 g/m². E, se for assim, com o devido respeito, não me parece que seja justificado o preço de R$ 324,94.

    Para piorar, no meu caso específico, o livro havia chegado com algumas das primeiras páginas um pouco amassadas e dobradas, fazendo com que essa obra perdesse ainda mais o brilho aos meus olhos. Por isso, eu a devolvi e pedi reembolso, que felizmente foi aceito pela Amazon sem problemas.
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  • C. Stefan
    5.0 out of 5 stars Life changing
    Reviewed in Germany on September 14, 2024
    Verified Purchase
    David MacKay mastered in every respect the art and craft of writing technical books. This particular textbook is as clear and entertaining as possible and I cannot recommend it highly enough.
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  • mhadi shateri
    5.0 out of 5 stars Very Nice Book to read
    Reviewed in Canada on July 16, 2021
    Verified Purchase
    It is a great book to read and enjoy
    Report
  • 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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