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Pattern Recognition and Machine Learning
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, 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 models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This 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, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
738 pages, Hardcover
Published August 17, 2006
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Displaying 1 - 30 of 84 reviews
September 25, 2009
Even with the help of a nuclear physicists turned neurophysiology data analyst, I couldn’t work beyond the first four chapters, and perhaps only a percentage of those. However, the efforts are rewarding. If you have read the entirety of this book, and understand it, then I would very much like to replace part of my brain with yours.
Want to read
March 12, 2010Dave, who knows about these things, recommended it... I have just ordered a copy.
Read
July 31, 2011For beginners who need to understand Bayesian perspective on Machine Learning, I'd would say that's the best so far. The author has make good attempt to explain complicated theories in simplified manner by giving examples/applications. The best part of the book are chapters on graphical models (chapter 8), mixture model EM (chap 9) and approximate inference (chap 10). The reason I didn't give 5 stars because it is too narrow a perspective on Machine Learning (only from Bayesian Perspective) that I feel did not terms well with the book title. Statistical learning and non-Bayesian perspective on machine learning are not covered much here. To make up for this discrepancies Tom Mitchell's Machine Learning does better job. Nevertheless, it still a great book to put on the shelve for machine learning.
March 8, 2021
Timeless, towering. My yardstick: The first time I read it (looked at it) I was way out of my depth and understood little. Year by year I misunderstand less of it.
September 3, 2012
1. The book is mainly about Bayesian approach. And many important techniques are missing. This is the biggest problem I think.
2. “Inconsistent difficulty”, too much time spent on simple things and very short time spent on complicated stuff.
3. Lack of techniques demonstration on real world problems.
2. “Inconsistent difficulty”, too much time spent on simple things and very short time spent on complicated stuff.
3. Lack of techniques demonstration on real world problems.
March 27, 2020
A concepts-oriented textbook about Machine Learning, relatively detailed considering the breadth of topics it covers, and suitable for text-study.
I would not recommend this book as the first to be introduced to Machine Learning, because it tends to go down rabbit holes of technical calculations, which makes things very concrete, but makes it difficult for the reader to keep track of what problem we're solving and to take a step back. I've found MacKay's Information Theory, Inference and Learning Algorithms to be more insightful, and (surprisingly) Manning's Introduction to Information Retrieval to do a better job at motivating and illustrating ML problems and approaches from the ground up.
To me, PRML really shines as a resource to go deeper after an introdution, with a technical exposition that is both detailed and general-purpose, and a wealth of exercises for self-study (highly appreciated!). It's especially relevant if you're interested in Bayesian approaches. It fits as a good stepping stone, right after conceptual introductions, and before more specialized material such as Deep Learning or Gaussian Processes for Machine Learning.
One could probably position PRML as the Bayesian counterpart to The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
Some specifics:
1. This is NOT a practical resource on ML, in particular it will not teach or demonstrate any software tool.
2. Contains many exercises, a good deal of them have available corrections, so it's suitable for self-study.
3. Does introduce Neural Networks, but won't go beyond the basic architectures. Does also introduce "classical" ML techniques such as linear models, SVMs, Gaussian Processes, etc.
4. The use of Graphical Models as a modeling tool for a broad range of situations is particularly insightful.
5. It's quite a long read - don't feel like you have to read all of it, it can fruitfally be used as reference material. The introduction chapter on its own is extremely insightful - to read and re-read.
I would not recommend this book as the first to be introduced to Machine Learning, because it tends to go down rabbit holes of technical calculations, which makes things very concrete, but makes it difficult for the reader to keep track of what problem we're solving and to take a step back. I've found MacKay's Information Theory, Inference and Learning Algorithms to be more insightful, and (surprisingly) Manning's Introduction to Information Retrieval to do a better job at motivating and illustrating ML problems and approaches from the ground up.
To me, PRML really shines as a resource to go deeper after an introdution, with a technical exposition that is both detailed and general-purpose, and a wealth of exercises for self-study (highly appreciated!). It's especially relevant if you're interested in Bayesian approaches. It fits as a good stepping stone, right after conceptual introductions, and before more specialized material such as Deep Learning or Gaussian Processes for Machine Learning.
One could probably position PRML as the Bayesian counterpart to The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
Some specifics:
1. This is NOT a practical resource on ML, in particular it will not teach or demonstrate any software tool.
2. Contains many exercises, a good deal of them have available corrections, so it's suitable for self-study.
3. Does introduce Neural Networks, but won't go beyond the basic architectures. Does also introduce "classical" ML techniques such as linear models, SVMs, Gaussian Processes, etc.
4. The use of Graphical Models as a modeling tool for a broad range of situations is particularly insightful.
5. It's quite a long read - don't feel like you have to read all of it, it can fruitfally be used as reference material. The introduction chapter on its own is extremely insightful - to read and re-read.
October 4, 2013
I must say this is a pretty painful read. Some parts seem to go very deep without much purpose, some topics which are pretty wide and important are skipped over in a paragraph. Maybe this book needs to go together with a taught course on the topic. On itself it is just too much.
Read
September 2, 2013recommended reading on machine learning from Gatsby (the neuroscience group in London, not the fictional Roaring 20s tail-chaser)
February 19, 2018
Took me a year to finish this book :D
May 26, 2020
An amazing textbook that would never get old.
May 2, 2025
- Covers import basics from statistics and probability theory
- Most evergreens in the methods very well explained
- Contains a lot of "outdated" methods, that may or may not give useful intuitions for more modern approaches
- Newer book, Deep Learning by Bishop & Bishop, may bring you more efficiently to the frontier of ML research
- Very readable and engaging style of writing
- Most evergreens in the methods very well explained
- Contains a lot of "outdated" methods, that may or may not give useful intuitions for more modern approaches
- Newer book, Deep Learning by Bishop & Bishop, may bring you more efficiently to the frontier of ML research
- Very readable and engaging style of writing
August 12, 2020
I started reading this book about 2 years too late, in my last year of my computer science degree. I have only now finished it, and I had to skim some of the last chapters. It's a pretty monumental task to read it through, and I cannot help but wonder how much it have taken to write it. Bishop has extraordinary insight into the Bayesian treatment in pattern recognition, and this is expressed here in, sometimes excruciating, details. If you're a beginner, I would just read the first 4 or so chapters, maybe chapter 8 and skim some of the variational inference sections. For more advanced learners, the later chapters provide some excellent detail on how to go beyond the basics.
I'm a little sad that this book was not a part of my official coursework, as I have only later discovered how relevant much of the content was for a significant part of my courses, and even worse, my thesis (where variational autoencoders and, hidden markov models and Bayesian ensemble models were at the center, all of which are either described directly in this book, or given foundation). The variational autoencoder would fit right in (which rose to prominence after the book was written).
Chapter 5 on neural networks is good, but it feels disconnected from the rest of the book. Still, it's a good chapter in itself, and even though a lot is happening and has happened since the chapter was written, the foundations described here remain the same. People might use ReLU as activation now, and there are a few new tricks, but the foundations remain the same, such as perceptrons, backpropagation and activation functions.
Bishop is not the most pedagogical author, especially if you read more than the first few chapters, so if you need someone to hold your hand while reading, this is probably not the best place to start. In any case, the book seems great as a reference and if you like this kind of stuff, you should definitely read it at some point.
I'm a little sad that this book was not a part of my official coursework, as I have only later discovered how relevant much of the content was for a significant part of my courses, and even worse, my thesis (where variational autoencoders and, hidden markov models and Bayesian ensemble models were at the center, all of which are either described directly in this book, or given foundation). The variational autoencoder would fit right in (which rose to prominence after the book was written).
Chapter 5 on neural networks is good, but it feels disconnected from the rest of the book. Still, it's a good chapter in itself, and even though a lot is happening and has happened since the chapter was written, the foundations described here remain the same. People might use ReLU as activation now, and there are a few new tricks, but the foundations remain the same, such as perceptrons, backpropagation and activation functions.
Bishop is not the most pedagogical author, especially if you read more than the first few chapters, so if you need someone to hold your hand while reading, this is probably not the best place to start. In any case, the book seems great as a reference and if you like this kind of stuff, you should definitely read it at some point.
January 25, 2018
Very decent mathematical overview of Data Science/ML with an emphasis on variational methods. It is particularly good intro to Bayesian stats/philosophy with nice pictures which is a good for those who don't know stats that well but are scientists at heart.
I enjoyed it but I also recommended it many times over to friends who knew far less stats than me and they often were extremely compelled by it (good for teaching).
It is an intro book, just to note.
I enjoyed it but I also recommended it many times over to friends who knew far less stats than me and they often were extremely compelled by it (good for teaching).
It is an intro book, just to note.
August 14, 2007
Being a new text, topics in modern machine learning research are covered. Bishop prefers intuitive explanations with lots of figures over mathematical rigor (Which is fine by me! =). A sample chapter is available at Bishop's website.
March 31, 2019
One of my first book on machine learning, this book can be painful if you don't have a solid background in algebra.
November 25, 2017
This book attempts to be self-contained, e.g: starting from probability and the Bayes' theorem as the foundation. But it is by no means an introductory book. If you have not developed an intuition for statistics and probability, you will find this book a very painful read.
This being said, I think you might want to use other books in combination with this book as reference to make the process a little bit easier.
In addition, some people have put together code (look for PRMLT on GitHub) in Matlab, that help illustrating the concepts in terms of code that you can experiment with, and seeing some of the concepts of this book at work.
This being said, I think you might want to use other books in combination with this book as reference to make the process a little bit easier.
In addition, some people have put together code (look for PRMLT on GitHub) in Matlab, that help illustrating the concepts in terms of code that you can experiment with, and seeing some of the concepts of this book at work.
July 11, 2015
I consider PRML one of the classic machine learning text books despite its moderate age (only 10 years). The book presents the probabilistic approach to modelling, in particular Bayesian machine learning. The material seems quite intimidating for readers that come from a not-so-strong mathematical background. But once you get over the initial inertia and practice deriving the equations on your own, you'll get a deep understanding of the content.
March 30, 2020
Slightly dense textbook (in terms of algebra, theory and also to read) and not very well structured in terms of concepts, best to be read alongside a taught course imo. Also narrow, only focuses on Bayesian approaches. However, very comprehensive on Bayesian ML and has some great, clear diagrams that really help learning.
February 3, 2013
Apply Bayesian reasoning to anything. Not for beginners but after reading 10 times it gets clearer ;). This was the book in my machine learning course and it was hard to process, but worth it.
January 26, 2017
I'm not read whole of book but i believe this is book is one of best reference for machine learning.
One of weak points is Deep learning not presented.
One of weak points is Deep learning not presented.
May 20, 2019
Read it if you want to really understand statistical learning. A fundamental book about fundamental things.
It is not the easy one but it will pay off.
It is not the easy one but it will pay off.
November 1, 2021
I read this textbook in parallel to my Machine Learning lectures, and it was what I needed to get a deeper understanding of the underlying mathematical and probabilistic concepts, the methodologies and the techniques currently used in Pattern Recognition.
The book covers the foundations of Machine Learning, as well as more advanced Pattern Recognition techniques. The writing is comprehensible and not extremely dry, the book structure makes sense and the topics follow a natural order. I can't say I understood everything, and I wasn't expecting to - learning isn't linear and that's okay.
Each chapter of the book builds up your understanding of the techniques, starting with the basics and progressing towards more advanced topics, in a way that is (relatively) easy to follow.
Such topics include Bayesian, linear, and nonlinear classifiers, clustering, feature selection and generation, classifier evaluation, as well as more specific methods, and doesn't fail to outline their applications.
The math, while pretty advanced, is very well explained, and with enough focus, you can follow it step by step while it leads you to the formulas that underlie the algorithms and methods used in practice.
The book covers the foundations of Machine Learning, as well as more advanced Pattern Recognition techniques. The writing is comprehensible and not extremely dry, the book structure makes sense and the topics follow a natural order. I can't say I understood everything, and I wasn't expecting to - learning isn't linear and that's okay.
Each chapter of the book builds up your understanding of the techniques, starting with the basics and progressing towards more advanced topics, in a way that is (relatively) easy to follow.
Such topics include Bayesian, linear, and nonlinear classifiers, clustering, feature selection and generation, classifier evaluation, as well as more specific methods, and doesn't fail to outline their applications.
The math, while pretty advanced, is very well explained, and with enough focus, you can follow it step by step while it leads you to the formulas that underlie the algorithms and methods used in practice.
July 6, 2025
"Pattern Recognition and Machine Learning" by Christopher Bishop is a fantastic book for anyone who loves diving into the theory behind machine learning. It’s perfect for PhD students or researchers who enjoy exploring the math and Bayesian side of things. The book covers topics like graphical models, Bayesian inference, and algorithms like EM, making complex ideas clear with great examples especially in chapters 8 to 10, which are really impressive.If you’re into AI and cybersecurity, this book is a solid starting point for understanding pattern recognition, which is super useful for analyzing security data or spotting cyber threats. That said, if you’re looking for practical, hands-on stuff or non-Bayesian methods, you might need another book, like Tom Mitchell’s Machine Learning, to round things out. It’s a great resource for research, teaching, or just deepening your knowledge, and definitely deserves a spot on your shelf.
November 9, 2021
Incredibly good! Artificial intelligence is pattern recognition.
From the book's Amazon page:
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.
From the book's Amazon page:
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.
January 29, 2022
Nice book about machine learning and pattern recognition concepts, machine learning is future of technologies ,
good to read about machine learning ..
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good to read about machine learning ..
commenting my website where i also shares tutors about machine learning course ..
Learn the syntactical application of python in data science. Get to grips with statistics, probability, and core mathematical concepts, which are the foundations of data science.
data science online classes
online data science degree
data science course fees
May 20, 2020
First off, it needs to be noted that there are things about this book that are old and should be ignored. Deep learning, and anything involving that, has went way beyond this. The neural network discussion is very old.
Some of the approaches it discusses are also largely out of favor, as they've been supplanted by other technologies. But things sometimes come around again.
Beyond that, though, there's a lot of good fundamentals that haven't changed so much.
As other reviewers note, it is a heavily Bayesian approach, which is something I like.
I read it a long time ago, was good then, still reads well.
Some of the approaches it discusses are also largely out of favor, as they've been supplanted by other technologies. But things sometimes come around again.
Beyond that, though, there's a lot of good fundamentals that haven't changed so much.
As other reviewers note, it is a heavily Bayesian approach, which is something I like.
I read it a long time ago, was good then, still reads well.
November 20, 2024
The chapters on regression, logistic regression, clustering algorithms, and on elementary probability theory were great; the same goes for the chapter on graphical models. But some of the other chapters, particularly the ones on Gaussian processes were well-nigh incomprehensible: had I only used this textbook for my statistical machine learning course, I probably would have failed (though it's possible, but unlikely, that I still might fail: the subject is very difficult if you have not done advanced probability theory and linear algebra before). Decent as a reference book but not as an introduction to machine learning!
December 12, 2024
Didn't read from end-to-end. However I needed to write how helpful this book was for the Mixture Model, Graphical Models and Sampling Methods.
As a reader who only had frequentist statistics course before, I sometimes doubt that I can read in English but, when you don't give up everything clicks!
I agree with the reviews of the need for more real-life examples for more solid understanding. There are some, for the probability distributions but they go sparse towards more advanced topics.
What made the reading process more effective was, to go back to the chapters that has been referenced. Frequently, sentences are condensed and explain double as you've expected.
As a reader who only had frequentist statistics course before, I sometimes doubt that I can read in English but, when you don't give up everything clicks!
I agree with the reviews of the need for more real-life examples for more solid understanding. There are some, for the probability distributions but they go sparse towards more advanced topics.
What made the reading process more effective was, to go back to the chapters that has been referenced. Frequently, sentences are condensed and explain double as you've expected.
July 4, 2021
This is a kind of a cheat as a science book snuck into my summer reading pile. However I’m reading it to broaden my horizons, not for an exam of sorts, so I think it counts. This is not a book that person can learn how to do machine learning from scratch. However, especially if you are not afraid of mathematics you can understand how does it work. Bishop frames most of machine learning models in in a probabilistic, Bayesian framework. For me it is attractive, however computational methods are a bit dated, as a lot has happened since 2006. Strong recommendation from me.
March 30, 2019
Actual Rating: 4.5
Recommended for understanding the Bayesian perspective of Machine Learning algorithms but it doesn't give a comparative analysis with Frequentist approach. Good for learning the (theoretical or ) mathematical aspects of algorithms and their graphical representation. Focus on real world applications missing.
P.S.: Used for teaching Bayesian Statistics and Machine Learning course for graduate students
Recommended for understanding the Bayesian perspective of Machine Learning algorithms but it doesn't give a comparative analysis with Frequentist approach. Good for learning the (theoretical or ) mathematical aspects of algorithms and their graphical representation. Focus on real world applications missing.
P.S.: Used for teaching Bayesian Statistics and Machine Learning course for graduate students
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