Science

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

  • Downloads：1923
  • Type：Epub+TxT+PDF+Mobi
  • Create Date：2022-05-09 06:52:41
  • Update Date：2025-09-07
  • Status：finish
  • Author：Christopher M. Bishop
  • ISBN：8132209060
  • Environment：PC/Android/iPhone/iPad/Kindle
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Summary

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。

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Reviews

Skillovilla ，

Nice book about machine learning and pattern recognition concepts， machine learning is future of technologies ，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 Nice book about machine learning and pattern recognition concepts， machine learning is future of technologies ，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 。。。more

Howard B ，

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 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。 。。。more

Andreea ，

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 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。 。。。more

Şafak Bilici ，

I have been reading this book for ~2 years。。。 Impossible to finish。。。

Francis Jones ，

This is a core textbook for anyone who wants to learn about machine learning from a fundamental， rigorous math perspective。 It’s essential reading for professionals in the field though probably not as a first step into the field。

Dhiraj Kumar ，

Most comprehensive book on Machine Learning。 One needs to know mathematics， particularly Linear Algebra， Probability， Calculus， to understand and appreciate this book。 I will keep coming back to this book in future。

Avishek Nag ，

I observed that people often do mistake by reading this book at first hand in Machine Learning。 There's the confusion。 This is not at all a beginners' book。 You really need some yrs of exp in ML to fully comprehend the breadth & depth of the book。 I agree that the language used may sound little complex， but you should not give up there。 And one thing， this book is not for people looking for hands on exp。 No。 This one is for somebody who is really interested in the core meaty math stuff and "have I observed that people often do mistake by reading this book at first hand in Machine Learning。 There's the confusion。 This is not at all a beginners' book。 You really need some yrs of exp in ML to fully comprehend the breadth & depth of the book。 I agree that the language used may sound little complex， but you should not give up there。 And one thing， this book is not for people looking for hands on exp。 No。 This one is for somebody who is really interested in the core meaty math stuff and "have some experience in ML"。 And you definitely need a pen & paper for reading it。 This is not for people looking for a crash course。 This book will definitely help you building a solid in & out understanding of the core math part of ML。 。。。more

Jerzy Baranowski ，

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 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。 。。。more

Valerie Dela Cruz ，

Very useful reference book for statistical learning！

KalteSterne ，

Good intro to Bayes ml

Emil Petersen ，

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 chapt 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。 。。。more

sarah chang ，

Such books outdate really fast。 But still an authoritative source for reference to basic principles。

Asim Bakhshi ，

An amazing textbook that would never get old。

John ，

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 heavi 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。 。。。more

Titouan ，

Good book but slow

Mazyar ，

Great book from great author。notice that It needs a strong background in linear algebra and statistics。

VW ，

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 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。 。。。more

Abdullah Shams ，

Will stay with me as a refrance book throughout my carrier。

El ，

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。

Bruno Centrone ，

Well written but mostly theoretical

Screw Driver ，

It's Bayesian！ It's Bayesian！ 。。。more

Ben Rey ，

It takes a ton of effort， focus and persistence to finish this textbook cover to cover。 The content is rich， and touches on many ML areas with a focus on the formulation and underpinnings of such algorithms。 Very fulfilling once finished。 👌

Ibrahim Sharaf ElDen ，

Focusing too much on the Bayesian approach， can be very hard if your mathematics （esp。 probabilities） foundations are not that solid。 Doesn't recommend it for people who are looking to start in machine learning， or learn about it from the practical side， the book is very theoretical。 Focusing too much on the Bayesian approach， can be very hard if your mathematics （esp。 probabilities） foundations are not that solid。 Doesn't recommend it for people who are looking to start in machine learning， or learn about it from the practical side， the book is very theoretical。 。。。more

Silvia Tulli ，

Machine Learning Bible

Oleg Dats ，

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。

Kent Sibilev ，

One of the best textbooks on ML。 My favorite topics of the books are Neural Networks， Graphical Methods， EM algorithm and one of the best introduction to Kernel Machines such as SVN and RVN。 The book takes very strong emphasis to Bayesian inference。

Rodrigo Rivera ，

Even more than 10 years after its publication， this book remains the best learning source for bayesian machine learning。 Clear explanations， colorful figures and a beautiful edition makes this book a truly classic。 Hope one day Chris Bishop gives us a second edition。

Weizhu Qian ，

It's a book for researchers who really want to understand machine learning。 It's a book for researchers who really want to understand machine learning。 。。。more

picoas picoas ，

If you're into stuff like this， you can read the full review。Ropey Lemmings: "Pattern Recognition and Machine Learning" by Christopher M。 BishopAs far as I can see Machine Learning is the equivalent of going in to B&Q and being told by the enthusiastic sales rep that the washing machine you are looking at is very popular （and therefore you should buy it too）。 Through clenched teeth I generally growl "That doesn't mean I think it is the best washing machine。" Following the herd is not my bag; the If you're into stuff like this， you can read the full review。Ropey Lemmings: "Pattern Recognition and Machine Learning" by Christopher M。 BishopAs far as I can see Machine Learning is the equivalent of going in to B&Q and being told by the enthusiastic sales rep that the washing machine you are looking at is very popular （and therefore you should buy it too）。 Through clenched teeth I generally growl "That doesn't mean I think it is the best washing machine。" Following the herd is not my bag; there are enormous problems down the line: the circular argument of how people make choices is strengthening its grip as real-time information （likes and dislikes） accelerate across social media networks。 。。。more

A Mig ，

Strong emphasis on the Bayesian viewpoint and heavy on equations。 The coloured panels with the short bio of famous statisticians and other important scientific figures were a welcomed addition to make the whole thing more digest。 So overall a difficult read， certainly not the easiest to learn all the basics but an excellent manual for the researcher looking for something specific， especially if Bayesian related。

Reference

google book https://www.google.com/search?tbm=bks&q=Pattern Recognition and Machine Learning

hathitrust digital library https://babel.hathitrust.org/cgi/ls?q1=Pattern Recognition and Machine Learning&field1=ocr&a=srchls&ft=ft&lmt=ft

The British Library https://bll01.primo.exlibrisgroup.com/discovery/search?query=any,contains,Pattern Recognition and Machine Learning&tab=LibraryCatalog&search_scope=Not_BL_Suppress&vid=44BL_INST:BLL01&lang=en&offset=0