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.
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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
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Most Helpful Customer Reviews
103 of 108 people found the following review helpful
By Sidhant
Format:Hardcover|Amazon Verified Purchase
This new book by Chris Bishop covers most areas of pattern recognition quite exhaustively. The author is an expert, this is evidenced by the excellent insights he gives into the complex math behind the machine learning algorithms. I have worked for quite some time with neural networks and have had coursework in linear algebra, probability and regression analysis, and found some of the stuff in the book quite illuminating.
But that said, I must point out that the book is very math heavy. Inspite of my considerable background in the area of neural networks and statistics, I still was struggling with the equations. This is certainly not the book that can teach one things from the ground up, and thats why I would give it only 3 stars. I am new to kernels, and I am finding the relevant chapters difficult and confusing. This book wont be very useful if all you want to do is write machine learning code. The intended audience for this book I guess are PhD students/researchers who are working with the math related aspects of machine learning. Undergraduates or people with little exposure to machine learning will have a hard time with this book. But that said, time spent in struggling with the contents of this book will certainly pay-off, not instantly though.
But that said, I must point out that the book is very math heavy. Inspite of my considerable background in the area of neural networks and statistics, I still was struggling with the equations. This is certainly not the book that can teach one things from the ground up, and thats why I would give it only 3 stars. I am new to kernels, and I am finding the relevant chapters difficult and confusing. This book wont be very useful if all you want to do is write machine learning code. The intended audience for this book I guess are PhD students/researchers who are working with the math related aspects of machine learning. Undergraduates or people with little exposure to machine learning will have a hard time with this book. But that said, time spent in struggling with the contents of this book will certainly pay-off, not instantly though.
288 of 320 people found the following review helpful
By dc
Format:Hardcover|Amazon Verified Purchase
I can appreciate others who might think that this is a great book.... but I am a student using it and I have some very different opinions of it.
First, although Mr. Bishop is clearly an expert in Machine Learning, he is also obviously a HUGE fan of Bayesian Statistics. The title of the book is misleading as it makes no mention of Bayes at all but EVERY CHAPTER ends with how all of the chapter's contents are combined in a Bayes method. That's not bad it's just not clear from the title. The title should be appended with "... using Bayesian Methods"
Second, while it is certainly a textbook, the author clearly has an understanding of the material that seems to undermine his ability to explain it. Though there are mentions of examples there are, in fact, none. There are many graphics and tiny, trivial indicators, but I can't help to think that every single one of the concepts in the book would have benefited from even a single application. There aren't any. I am lead to believe that if you are already aware of many of the methods and techniques that this would be an excellent reference or refresher. As a student starting out I almost always have no idea what his intentions are.
To make matter worse, he occasionally uses symbols that are flat-out confusing. Why would you use PI for anything other than Pi or Product? He does. Why use little k, Capital K, and Greek Letter Kappa (a K!) in a series of explanations. He does. He even references articles that he has written... in 2008!!
Every chapter seems to be an exercise to see how many equations he can stuff in it. There are 300 in Chapter 2 alone. Over and over and over again I have the feeling that he is trying to TELL me how to ride a bicycle when it would have been so much easier to at least let me see the view from behind the handle bars with my feet on the pedals. Chapter five on Neural Nets, for example, is abysmally over-complicated. Would you hand someone a dictionary and ask them to write a poem? ("Hey, all the words you need are in here!") Of course not.
Third, the book mentions that there is a lot of information available on the web site. The only info available on his website is a brief overview of the text, a detailed overview of the text (that's not a typo.... he has both), an example chapter, links to where the book can be purchased, and (actually, quite useful for creating slides) an archive of all of the figures available in the book. There are no answers to problems or explorations of any part of the material. The upcoming book might be amazing and exactly what I am looking for but it could be months away and another $50 or so to purchase it. Hardly ideal. How about putting some of that MatLab code on your site? *Something* to crystalize the concepts!
Finally, while the intro indicates this might be a good book for Computer Scientists it would actually make more sense to call it a Math book. More specifically a Statistics book. There are no methods, no algorithms, no bits of pseudo-code, and (again) no applications are in the text. Even examples that actually used hard numbers and/or elements from a real problem and explained would be much appreciated.
Maybe I am being a little critical and perhaps I want for too much but in my mind if you are writing a book with the goal of TEACHING a subject, it would be in your interest to make things clear and illustrative. Instead, the book feels more like a combination of "I am smart. Just read this!" and a reference text.
First, although Mr. Bishop is clearly an expert in Machine Learning, he is also obviously a HUGE fan of Bayesian Statistics. The title of the book is misleading as it makes no mention of Bayes at all but EVERY CHAPTER ends with how all of the chapter's contents are combined in a Bayes method. That's not bad it's just not clear from the title. The title should be appended with "... using Bayesian Methods"
Second, while it is certainly a textbook, the author clearly has an understanding of the material that seems to undermine his ability to explain it. Though there are mentions of examples there are, in fact, none. There are many graphics and tiny, trivial indicators, but I can't help to think that every single one of the concepts in the book would have benefited from even a single application. There aren't any. I am lead to believe that if you are already aware of many of the methods and techniques that this would be an excellent reference or refresher. As a student starting out I almost always have no idea what his intentions are.
To make matter worse, he occasionally uses symbols that are flat-out confusing. Why would you use PI for anything other than Pi or Product? He does. Why use little k, Capital K, and Greek Letter Kappa (a K!) in a series of explanations. He does. He even references articles that he has written... in 2008!!
Every chapter seems to be an exercise to see how many equations he can stuff in it. There are 300 in Chapter 2 alone. Over and over and over again I have the feeling that he is trying to TELL me how to ride a bicycle when it would have been so much easier to at least let me see the view from behind the handle bars with my feet on the pedals. Chapter five on Neural Nets, for example, is abysmally over-complicated. Would you hand someone a dictionary and ask them to write a poem? ("Hey, all the words you need are in here!") Of course not.
Third, the book mentions that there is a lot of information available on the web site. The only info available on his website is a brief overview of the text, a detailed overview of the text (that's not a typo.... he has both), an example chapter, links to where the book can be purchased, and (actually, quite useful for creating slides) an archive of all of the figures available in the book. There are no answers to problems or explorations of any part of the material. The upcoming book might be amazing and exactly what I am looking for but it could be months away and another $50 or so to purchase it. Hardly ideal. How about putting some of that MatLab code on your site? *Something* to crystalize the concepts!
Finally, while the intro indicates this might be a good book for Computer Scientists it would actually make more sense to call it a Math book. More specifically a Statistics book. There are no methods, no algorithms, no bits of pseudo-code, and (again) no applications are in the text. Even examples that actually used hard numbers and/or elements from a real problem and explained would be much appreciated.
Maybe I am being a little critical and perhaps I want for too much but in my mind if you are writing a book with the goal of TEACHING a subject, it would be in your interest to make things clear and illustrative. Instead, the book feels more like a combination of "I am smart. Just read this!" and a reference text.
50 of 54 people found the following review helpful
Format:Hardcover
The book is worth a look, but after some of 5 star reviews i read here, it was quite a disappointment. Yes, the book covers a lot of ground. Yes, the book has lots of nice pictures and easy examples, but that is exactly the problem. There are lots and lots of simple examples to explain the most basic concepts, but when it gets complicated the book often sounds as if the text was taken out of a mathematics book. For example: the basics of probability theory are introduced for over 5 pages with the example of "two coloured boxes each containing fruit". Nothing wrong with that. Then the chapter continues with probability densities which are covered within 2 pages and contain sentences like "Under a nonlinear change of variable, a probability density transforms differently from a simple function, due to the Jacobian factor". There is no mentioning how a simple function exactly transforms, what a Jacobian factor actually is and why we would be interested in a nonlinear change. Surely, some of the introductory pages could have been thrown out to explain in depth the more difficult issues. Unfortunately, this is not the only time, where easy concepts get a lot of attention and the truly important complex concepts are skimmed over. All in all, still worth a read, though do not expect too much.
Most Recent Customer Reviews
4.0 out of 5 stars
Great, necessary but to hard reading
This is a great reference on Pattern Recognition and, clearly, is based on others classical books on this theme (Richard O. Duda, for instance). Read more
Published 20 days ago by MARCO A COUTINHO
4.0 out of 5 stars
Great book on Bayesian Networks in a continuous and hybrid space
Very good book on probabilistic approach to machine learning. It goes from the elementary building blocks of probability distributions, up to the higher level frameworks of... Read more
Published 25 days ago by Daniel Korzekwa
5.0 out of 5 stars
Excellent reference book on machine learning - strong Bayesian...
I like this book and refer to it quite a bit. Pretty much all the background material one needs is in there. Read more
Published 1 month ago by Fred Richardson
5.0 out of 5 stars
amazing book!
book builds up on concepts from scratch. A novice like me could start to grasp knowledge about the subject. In depth coverage of topics.
Published 1 month ago by Anubhav Malhotra
4.0 out of 5 stars
Not for beginners or want to be's
The book is not written for a beginner or even an intermediate researcher. This is a very serious book on Pattern Recognition and Machine Learning. Read more
Published 1 month ago by J. Elliott
5.0 out of 5 stars
The PR bible
I used this work when i was doing my PhD and i just want to say it is the most complete book in PR and machine learning you can find. Be aware of the density of the book!
Published 3 months ago by Pau Baiget
1.0 out of 5 stars
Obfuscates things rather than explaining them
As a professor who have taught machine learning at the graduate level, I found this book one of the worst ever written for machine learning. Read more
Published 4 months ago by Liang Huang
5.0 out of 5 stars
Great book
Great book for machine learning
I want to bug another book
But the price is a little expensive
When does it has Chinese version?
I want to bug another book
But the price is a little expensive
When does it has Chinese version?
Published 5 months ago by Liu Yang
3.0 out of 5 stars
This is a decent book, but it is a hard read
This is quite a decent book (with respect to coverage), but it went too far in keeping the notation uncluttered. A lot of things discussed here are really simple. Read more
Published 5 months ago by A guy from nowhere
5.0 out of 5 stars
Must have
One of the best text books covering probabilistic background of learning in deep. Especially a good premier for one working on new algorithms and theoretical improvements. Read more
Published 6 months ago by Ahmet Hungari
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