Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop (PDF)

Description

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

User’s Reviews

Editorial Reviews: Review From the reviews:”This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas…A strong feature is the use of geometric illustration and intuition…This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group.” John Maindonald for the Journal of Statistical Software”In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields.” (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007)”This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study.” (W. R. Howard, Kybernetes, Vol. 36 (2), 2007)”Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals.” (C. Tappert, CHOICE, Vol. 44 (9), May, 2007)”The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … .” (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007)”This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value.” (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007)”Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record.” (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008)”This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … .” (L. State, ACM Computing Reviews, October, 2008)”Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts.” (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008) From the Back Cover The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.Christopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook “Neural Networks for Pattern Recognition” has been widely adopted.Coming soon:*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked “www” in the text)*For instructors, worked solutions to remaining exercises from the Springer web site*Lecture slides to accompany each chapter*Data sets available for download About the Author ​Chris Bishop is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh. Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐First of all, as some other reviewers have pointed out, the subtitle of the book should include the word ‘Bayesian’ in some form or the other. The reason this is important is because the Bayesian approach, although an important one, is not adapted across the board in machine learning, and consequently, an astonishing number of methods presented in the book (Bayesian versions of just about anything) are not mainstream. The recent Duda book gives a better idea of the mainstream in this sense, but because the field has evolved in such rapidity, it excludes massive recent developments in kernel methods and graphical models, which Bishop includes.Pedagogically, however, this book is almost uniformly excellent. I didn’t like the presentation on some of the material (the first few sections on linear classification are relatively poor), but in general, Bishop does an amazing job. If you want to learn the mathematical base of most machine learning methods in a practical and reasonably rigorous way, this book is for you. Pay attention in particular to the exercises, which are the best I’ve seen so far in such a text; involved, but not frustrating, and always aiming to further elucidate the concepts. If you want to really learn the material presented, you should, at the very least, solve all the exercises that appear in the sections of the text (about half of the total). I’ve gone through almost the entire text, and done just that, so I can say that it’s not as daunting as it looks. To judge your level regarding this, solve the exercises for the first two chapters (the second, a sort of crash course on probability, is quite formidable). If you can do these, you should be fine. The author has solutions for a lot of them on his website, so you can go there and check if you get stuck on some.As far as the Bayesian methods are concerned, they are usually a lot more mathematically involved than their counterparts, so solving the equations representing them can only give you more practice. Seeing the same material in a different light can never hurt you, and I learned some important statistical/mathematical concepts from the book that I’d never heard of, such as the Laplace and Evidence Approximations. Of course, if you’re not interested, you can simply skip the method altogether.From the preceding, it should be clear that the book is written for a certain kind of reader in mind. It is not for people who want a quick introduction to some method without the gory details behind its mathematical machinery. There is no pseudocode. The book assumes that once you get the math, the algorithm to implement the method should either become completely clear, or in the case of some more complicated methods (SVMs for example), you know where to head for details on an implementation. Therefore, the people who will benefit most from the book are those who will either be doing research in this area, or will be implementing the methods in detail on lower level languages (such as C). I know that sounds offputting, but the good thing is that the level of the math required to understand the methods is quite low; basic probability, linear algebra and multivariable calculus. (Read the appendices in detail as well.) No knowledge is needed, for example, of measure-theoretic probability or function spaces (for kernel methods) etc. Therefore the book is accessible to most with a decent engineering background, who are willing to work through it. If you’re one of the people who the book is aimed at, you should seriously consider getting it.Edited to Add:I’ve changed my rating from 4 stars to 5. Even now, 4-5 years later, there is simply no good substitute for this book.

⭐Usually considered to be a branch of artificial intelligence, especially at the present time, pattern recognition is defined in this book as the automatic discovery of regularities in data by the use of computer algorithms and the use of these regularities for classifying the data in different categories. The first part of this definition is typically referred to as `unsupervised learning’ and the latter `supervised learning.’ Both of these areas have resulted in a gargantuan amount of research due to their importance in areas such as medicine, genomics, network modeling, financial engineering, and voice recognition. This book emphasizes a “conceptual” approach to teaching pattern recognition, and therefore is highly valuable to those who need to learn the subject. Too often this field is taught purely from the formal standpoint, or conversely by the use of many trivial examples that illustrate the algorithms that are used. These approaches make the subject appear to be either a highly-developed mathematical one (which it is) or a cookbook that does not have a sound foundation. This book is one of the few that will allow the reader to gain a more in-depth understanding and appreciation of the subject as preparation for doing research and development in pattern recognition. The author claims that the book is self-contained as far as background in probability theory is concerned, but readers should still be prepared with this background in order to better appreciate the content. The Bayesian paradigm dominates the book, as it should given the current emphasis in research circles.Some of the highlights of the book include discussions on:* Relative entropy and mutual information. These two concepts have become very important in recent years, especially in the validation of pattern recognition models, the selection of relevant variables, and in independent component analysis.* Periodic variables and how they can be used in contexts where Gaussian distributions are problematic.* Markov chain Monte Carlo sampling, especially the role of the detailed balance condition in obtaining the acceptance probability for the Metropolis-Hastings algorithm.* Bayesian linear regression and its ability to deal with the over-fitting problem in calculations of maximum likelihood and the determination of model complexity.* Kernel learning (usually called support vector machines in other books).Some of the minuses of the book include:* Needs more in-depth discussion of Bayesian neural networks, over and above what is done in the book. The author’s does devote a section in the book to this topic, but given its enormous importance, especially in automated learning and economic forecasting, more examples need to be included.* More real-world test cases need to be included, along with a comparison of the efficacies of different approaches, so as to illustrate the “no free lunch” philosophy.* More exercises that require more analysis on part of the reader, instead of derivation-type problems or straightforward numerical exercises.* Needs more details on independent component analysis. Only a few paragraphs are devoted to this important topic.

⭐There are a huge number of machine learning books now available. I own many of them. But I don’t think any have had such an impact as Chris Bishop’s effort here – I certainly count it as my favourite. The material covered is not exhaustive (although good for 2006), but it’s a good springboard to many other advanced texts. (The moniker of ML ‘Bible’ has apparently been passed to Kevin Murphy’s book.) What *is* covered is explained with exceptional clarity with an eye for understanding the intuition as well as the theory.If you are after a practitioners guide, or a first ML book for self study, this probably isn’t ideal. It assumes significant familiarity with multivariate calculus, probability and basic stats (identities, moments, regression, MLE etc.). The pitch is probably early post-graduate level, but with a few stretching parts. If this is your background, I think it’s a better first ML book than MacKay (Information Theory …), Murphy (Machine Learning …), or Hastie et al. (Elements of Statistical Learning), due to its coherence of topics and consistency of depth. But those books are all excellent in their own ways. However, Barber (Bayesian Reasoning …) is a good alternative.Most chapters are fairly self contained, so once you’ve worked your way through the first couple of chapters, you can skip around as required. A particular highlight for me were the chapters on EM and variational methods (ch 9 & 10); I think you’d be hard pressed to find a better explanation of either of them. Finally, worth pointing out it’s unrepentantly Bayesian, and lacking some subtelty which may be grating for seasoned statisticians. Nevertheless, if the above sounds like what you’re looking for, this is probably a good choice.

⭐It’s a must get for Machine Learning students. It covers every fundamental concept of ML. However, it is not quite beginner level friendly, meaning you are required to have some understanding of basic probability and linear algebra. I am giving four stars due to the way it’s printed. The print paper quality is good and I can confirm it is hardcover but the margin is bit unusual with wide space on the left hand side.

⭐This is a good in-depth book on pattern recognition. The only problem I have with it is that it can be a challenge to read. I find it easier to understand new mathematical techniques and equations when they come coupled with a good intuitive explanation for people who find it harder to just look at an equation and instantly “get it”. I find that this book isn’t as good at this as I would like, but it is so full of useful information that it’s still a great book.

⭐I take back my previous negative review (DHL returned without delivering to me for some reason not explained). I received the book today and very happy – exactly as expected – excellent quality!

⭐This is a great book with one of the most clear presentations of several fundamental algorithms. In my experience this is a book I keep coming back to.

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