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Information Theory, Inference and Learning Algorithms
Free Ebook Information Theory, Inference and Learning Algorithms
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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
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Book Description
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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Product details
Hardcover: 640 pages
Publisher: Cambridge University Press; 1 edition (October 6, 2003)
Language: English
ISBN-10: 0521642981
ISBN-13: 978-0521642989
Product Dimensions:
7.7 x 1.3 x 10 inches
Shipping Weight: 3.3 pounds (View shipping rates and policies)
Average Customer Review:
4.1 out of 5 stars
29 customer reviews
Amazon Best Sellers Rank:
#110,289 in Books (See Top 100 in Books)
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.
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.
If someone looking for a different perspective, interesting and challenging, this is a book to read.
Good book on topic, well written.
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.
A sense of humor and a wide-ranging yet clear presentation.
I used this for a course on Information Theory, and it was much better than Cover & Thomas because it provided more background and motivation for the material.
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.
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