# Pattern Recognition and Machine Learning (Information Science and Statistics) E–pub READ

- 738pages
- Pattern Recognition and Machine Learning (Information Science and Statistics)
- Christopher M. Bishop
- English
- 22 July 2018
- 0387310738

## Christopher M. Bishop ë 9 characters

Christopher M. Bishop ë 9 characters Free download ì E-book, or Kindle E-pub ë Christopher M. Bishop review Æ Pattern Recognition and Machine Learning (Information Science and Statistics) ↠ E-book, or Kindle E-pub T 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 wi. In general most of the topics are not clearly explained the chapters are not self contained In addition most of the problems at the end of the chapters consist in completing the steps between the book s euations which I think is not very didactic since it s just completing a bit of algebra There are very few problems that really put you to think Annie probability distributions when no other books apply graphical models to machine learning No Back Alley Slut previous knowledge of The Collected Works of DrPatrick Carnes pattern recognition or machine learning concepts is assumed Familiarity wi. In general most of the topics are not clearly explained the chapters are not self contained In addition most of the Revolution in Manufacturing Single minute Exchange of Die System problems at the end of the chapters consist in completing the steps between the book s euations which I think is not very didactic since it s just completing a bit of algebra There are very few Longing Orgasmic Texas Dawn #2 problems that really Sight and Sound A Fiftieth Anniversary Selection put you to think

**Free download ì E-book, or Kindle E-pub ë Christopher M. Bishop**

Christopher M. Bishop ë 9 characters Free download ì E-book, or Kindle E-pub ë Christopher M. Bishop review Æ Pattern Recognition and Machine Learning (Information Science and Statistics) ↠ E-book, or Kindle E-pub 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 I. I recently had to uickly understand some facts about the probabilistic interpretation of pca Naturally I picked up this book and it didn t disappoint Bishop is absolutely clear and an excellent writer as wellIn my opinion despite the recent publication of Kevin Murphy s very comprehensive ML book Bishop is still a better read This is mostly because of his incredible clarity but the book has other virtues best in class diagrams judiciously chosen a lot of material very well organized excellent stage setting the first two chapters Now sometimes he s a bit cryptic for example the proof that various kinds of loss lead to conditional median or mode is left as an exercise ex 127 Murphy actually discusses it in some detail This is true in general Murphy actually discusses many things that Bishop leaves to the reader I thought chapters three and four could have been detailed but I really have no other complaintsPlease note that in order to get an optimal amount out of reading this book you should already have a little background in linear algebra probability calculus and preferably some statistics The first time I approached it was without any background and I found it a bit unfriendly and difficult this is no fault of the book however Still you don t need that much just the basicsUpdate I should note that there are some puzzling omissions from this book Eg f score PRML is relentlessly so ESL does not use graphical models or latent variables as a unifying perspective PRML does ESL is better on freuentist model selection including cross validation ch 7 I think PRML is better for graphical models Bayesian methods and latent variables which correspond to chs 8 13 and ESL better on linear models and density based methods and other stuff besides Finally ESL is way better on local models like kernel regression loess Your mileage may varyThey are both excellent books ESL seems a bit mathematically dense than PRML and is also better for people who are in industry as versus academia I was in the latter but now in the former

### Free download Pattern Recognition and Machine Learning (Information Science and Statistics)

Christopher M. Bishop ë 9 characters Free download ì E-book, or Kindle E-pub ë Christopher M. Bishop review Æ Pattern Recognition and Machine Learning (Information Science and Statistics) ↠ E-book, or Kindle E-pub Th multivariate calculus and basic linear algebra is reuired 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 theor. Overall I like the book and would recommend it But the exposition could use significant polishingPros not mathematically heavy lots of good heuristics that capture the math without delving too far in choice of topics and their discussion eg a great place to learn about kernel methods and graphical models easy to get hooked on if you mind the gapsCons read belowWhile the exposition is spotty compare eg with Feller or Gelman the author manages to follow a mostly linear exposition on fascinating topicsThe book would highly benefit from editing provided by someone with a solid math background In particular there are good mistakes than bad mistakes Often when speaking with people with stats background than me the conversation is isomorphic toMe therefore this statement is wrong I think what he meant wasBro Ah yes But you get it that s what he meantMe Then why didn t he write itBut at least dialogues like these help cement ideasPlease correct me if any of the following contentions are wrong I may update as I continue to readSome parts are not even wrong for exampleSec 21 paragraph above E 219We see that this seuential approach to learning arises naturally when we adopt a Bayesian viewpoint It is independent of the choice of prior and of the likelihood functions and depends only on the assumption of iid dataFirst if you follow the thread of this section and therefore go back to the contrived coin flipping example you would see that in the non Bayesian point of view estimates are also updated in a seuence of experiments Hence a Bayesian point of view in this case is no natural than a freuentist Second by definition of iid a single fixed distribution is postulated to exist and therefore a prior is in fact chosen how do you define a posterior without a prior But ok I think I get it a seuential approach fits in nicely with the Bayesian point of view I agree and that s all that needs to be saidMathematically wrongSame section following 235 the statements following E 220Note that in the limit of an infinitely large data set m l infinity the result 220 reduces to the maximum likelihood 28First if F is a function of x then the resulting limit for x to infinity must not involve x Plus the order andor direction of his m and l in the limit is ambiguous Second what he meant to say is that for m and l both sufficiently large compared with a and b we get that 220 reduces to 283rd paragraph before 22For a finite data set the posterior mean for mu always lies between the prior mean and the maximum likelihood estimate for mu corresponding to the relative freuencies of events given by 27Again we are told to forget that the choice of a prior makes a difference It seems the above statement is false we may choose a prior that is heavily weighted on a single point so that this prior s mean is greater than mleParagraphs directly above the beginning of 22In fact we might wonder whether it is a general property of Bayesian learning that as we observe and data the uncertainty represented by the posterior distribution will steadily decrease and then this result shows that on average the posterior variance of theta is smaller than the prior varianceThe result ie E 224 is an assertion of the form Suppose ab c 0 c is fixed and c a b Then if b goes up a must go down I don t see how this relates to what seemed to be his premise that increasing the size of a data set seuentially or not has the seemingly desired effect of reducing posterior variance I suspect there are in fact limiting results in special cases that show the desired steady reduction in posterior varianceI wish he would have referenced themSection 23 following E 244 we note that the matrix igma can be taken to be symmetricActually by definition any covariance matrix is symmetricI could go onAll this said it s worth repeatingI like the book and not only because its mistakes or sometimes shady logic encourage the interested reader to try and discover correctless wrong statements