information theory, inference and learning algorithms review

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The way the author establishes the relationship between Information theory, Inference and Learning is exceptional. I just love this book. Fortunately for me (having purchased it for ~50$), I have been gliding along at quite an easy pace. This book is amazing! Low-density parity-check codes (which are used in HDTV) are very cool! Disabling it will result in some disabled or missing features. Reviewed in the United States on June 18, 2015. If you are involved with, or interested in, high-end data analytics, then you _need_ this. but what I do not like is the way it is organized with. MacKay's coverage of this material is both conceptually clear and practically-minded, and helped me a great deal. The printing is bad. Superficially appealing but very hard to actually plod through, Reviewed in the United States on November 1, 2014. I am reviewing David MacKay's `Information Theory, Inference, and Learning Algorithms, but I haven't yet read completely. But for folks already versed in the topic, this book can shed a lot of new light and does a good job abstracting it with concepts from information theory and stats. One of the best books in machine learning, Reviewed in the United States on October 19, 2018. To see what your friends thought of this book, Information Theory, Inference and Learning Algorithms, NB: Both book and lectures are available for free online. Purchase of this book is not recommended. There's a problem loading this menu right now. Just a moment while we sign you in to your Goodreads account. I am reviewing David MacKay's `Information Theory, Inference, and Learning Algorithms, but I haven't yet read completely. Excellent service and condition of the book. One of the best introductions to information theory, coding (lossy and lossless) and Bayesian approaches to decoding and to inference. This page works best with JavaScript. He returned to Cambridge as a Royal Society research fellow at Darwin College. As a grad student in optimization with a background in physics, I really enjoy the multi-disciplinary approach of this book. This is an unqualified classic, to shelve with the likes of 'Structure and Interpretation of Computer Programs', 'Concrete Mathematics' and 'Mathematical Methods of Classical Mechanics'. Refresh and try again. Find helpful customer reviews and review ratings for Information Theory, Inference and Learning Algorithms at Amazon.com. Outstanding book, especially for statisticians, Reviewed in the United States on October 2, 2007. Reviewed as part of my 100 books challenge: One of the very rare academic texts which balances intuition and mathematical rigour. Much of the. Let us know what’s wrong with this preview of, Published Excellently written, would revisit again. Start by marking “Information Theory, Inference and Learning Algorithms” as Want to Read: Error rating book. (Check YouTube for lectures. Other reviewers have provided all the details you need to know before buying. According to the back cover, Bob McEliece, the author of a 1977 classic on information theory recommends you buy two copies, one for the office and one for home. overkill for engineers with a high erdos number, Reviewed in the United States on November 7, 2011. It combines so many interesting topics in an unified framework: Bayesian framework, from information theory to neuro network. October 6th 2003 Welcome back. 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. 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. One may not be used to getting both. by Cambridge University Press, Information Theory, Inference & Learning Algorithms. I've been working through this chapter by chapter for about a month now. Information theory and inference, often taught separately, are here united in one entertaining textbook. David MacKay was a Professor in the Department of Physics at the University of Cambridge. It will be years before I finish it, since it contains the material for several advanced undergraduate or graduate courses. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. This book actually uses a detailed description of those questions "left for the reader" as a way to reinforce its pedagogy. MacKay's coverage of this material is both conceptually clear and practically-minded, and helped me a great deal. A review of information theory, coding theory, and several machine learning and statistics topics, all from a Bayesian perspective. for example the first 3 chapters are nothing but restating of some later chapters. Coverage or detail? 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. also some theories and techniques are not simply described. It's been with me through several career shifts and has satisfied various, random fits of curiosity. This firmly grounds machine learning algorithms in a Bayesian paradigm and gives people the intuition for the subject. Disabling it will result in some disabled or missing features. I will order again. Purchase of this book is not recommended, Reviewed in the United States on December 5, 2017. Prime members enjoy FREE Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle books. Reviewed in the United States on February 8, 2017. Top subscription boxes – right to your door, Information Theory, Inference and Learning Algorithms, See all details for Information Theory, Inference and Learning Algorithms, © 1996-2020, Amazon.com, Inc. or its affiliates. However, I often am frustrated with the book's style. This page works best with JavaScript. I just wish I had the logical stamina to follow his arguments. You can still see all customer reviews for the product. Information theory and inference, often taught separately, are here united in one entertaining textbook. However, I often am frustrated with the book's style. I really enjoy(ed) working with this book. If you are looking for a simple introduction to Bayesian machine learning, this book is a perfect fit. 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. 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. Hokey the Bayesian Bear says: "Only you can prevent the misguided use of p-values.". Thank you. The printing is bad. Brilliantly exposited. A Bayesian View: Excellent Topics, Exposition and Coverage, Reviewed in the United States on November 20, 2008. Reviewed in the United States on July 8, 2015. Such a review, whether positive or critical, could not hope to give a complete picture of what this text actually is. 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. Interludes on crosswords, evolution, and sex provide entertainment along the way. Purchase of this book is not recommended. Reviewed in the United States on September 6, 2013. Goodreads helps you keep track of books you want to read. Reviewed in the United States on July 2, 2013. Already I've learnt about hamming codes and the formulas & axioms (interestingly formulated!) Fortunately for me (having purchased it for ~50$), I have been gliding along at quite an easy pace. He studied Natural Sciences at Cambridge and then obtained his PhD in Computation and Neural Systems at the California Institute of Technology. To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Alas the maths undergrad me would be so disappointed. David MacKay was a Professor in the Department of Physics at the University of Cambridge. Reviewed in the United States on November 15, 2017. He studied Natural Sciences at Cambridge and then obtained his PhD in Computation and Neural Systems at the California Institute of Technology. This is a really good book. An absolute joy to read. The problem sections are not just great, they are absolutely worth doing. This textbook introduces theory in tandem with applications. Unbelievably clear thinker. It also analyzes reviews to verify trustworthiness. This textbook introduces th. The treatment probably isn't the most sophisticated, I'm sure, but for me at least it's a good enough fit. 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.

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