--- product_id: 2214052 title: "Pattern Recognition and Machine Learning (Information Science and Statistics)" price: "NZ$240" currency: NZD in_stock: true reviews_count: 13 url: https://www.desertcart.nz/products/2214052-pattern-recognition-and-machine-learning-information-science-and-statistics store_origin: NZ region: New Zealand --- # Pattern Recognition and Machine Learning (Information Science and Statistics) **Price:** NZ$240 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Pattern Recognition and Machine Learning (Information Science and Statistics) - **How much does it cost?** NZ$240 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.nz](https://www.desertcart.nz/products/2214052-pattern-recognition-and-machine-learning-information-science-and-statistics) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## 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. Review: Excellent text - 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. Review: Still (one of) the best - I recently had to quickly 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 well. In 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 1.27). 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 more detailed, but I really have no other complaints. Please 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 basics. Update: I should note that there are some puzzling omissions from this book. E.g. f-score & confusion matrices are not mentioned (see Murphy section 5.7.2) - it would have been very natural to mention these concepts in ch 1, along with decision theory. Nor is there much on clustering, except for K-means (see Murphy ch 25). Not a huge deal, it's easy to get these concepts from elsewhere. I recommend using Murphy as and when you need, to fill in gaps. One more update: I've been getting into Hastie et al's ESL recently, and I'm really impressed with it so far - I think the practitioner should probably get familiar with both ESL and PRML, as they have complementary strengths and weaknesses. ESL is not very Bayesian at all; PRML is relentlessly so. ESL does not use graphical models or latent variables as a unifying perspective; PRML does. ESL is better on frequentist 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 vary...They are both excellent books. ESL seems a bit more 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), ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #118,227 in Books ( See Top 100 in Books ) #16 in Computer Vision & Pattern Recognition #82 in Probability & Statistics (Books) #311 in Artificial Intelligence & Semantics | | Customer Reviews | 4.5 out of 5 stars 793 Reviews | ## Images ![Pattern Recognition and Machine Learning (Information Science and Statistics) - Image 1](https://m.media-amazon.com/images/I/71fqxXDY2ZL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Excellent text *by K***A on February 22, 2008* 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. ### ⭐⭐⭐⭐⭐ Still (one of) the best *by E***E on January 17, 2016* I recently had to quickly 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 well. In 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 1.27). 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 more detailed, but I really have no other complaints. Please 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 basics. Update: I should note that there are some puzzling omissions from this book. E.g. f-score & confusion matrices are not mentioned (see Murphy section 5.7.2) - it would have been very natural to mention these concepts in ch 1, along with decision theory. Nor is there much on clustering, except for K-means (see Murphy ch 25). Not a huge deal, it's easy to get these concepts from elsewhere. I recommend using Murphy as and when you need, to fill in gaps. One more update: I've been getting into Hastie et al's ESL recently, and I'm really impressed with it so far - I think the practitioner should probably get familiar with both ESL and PRML, as they have complementary strengths and weaknesses. ESL is not very Bayesian at all; PRML is relentlessly so. ESL does not use graphical models or latent variables as a unifying perspective; PRML does. ESL is better on frequentist 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 vary...They are both excellent books. ESL seems a bit more 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), ### ⭐⭐⭐⭐⭐ If only all textbooks were this well-written *by S***S on January 29, 2007* I was a big fan of Bishop's earlier "Neural Networks for Pattern Recognition" despite my not being particularly interested in neural networks (as opposed to other aspects of machine learning), and so I was pretty excited when I heard about this book. Reading it has not left me disappointed. Like his earlier book, this text is quite mathematically oriented, and not well-suited for people who aren't comfortable with calculus. However, also like in "NNPR", the writing style here is very clear, and everything past basic calculus and linear algebra is well-explained before it's needed. The appendices alone are a goldmine. (Appendix B is a great "cheat sheet" for commonly used probability distributions; Appendix C has lots of useful matrix properties you may have forgotten or never known; Appendix D quickly explains what you need to know about the calculus of variations; and Appendix E does the same for Lagrange multipliers.) The author also does an excellent job throughout the text of marrying math and intuition without giving either short shrift. However, note that the material covered is inherently pretty complex, so the book can still be intimidating in parts despite the excellent writing. It's more appropriate for, say, Ph.D. students and professional researchers in statistics or machine learning than people who just want to crank out code for a simple classifier. There is very little pseudocode (although copious MATLAB code will supposedly be made available in a companion book due out in 2008), and the book's overall approach to machine learning is basically hard-core Bayesian statistics. If you are not willing to scratch your head for a while over lots and lots of equations, this may not be the book for you. On the flip side, people who are already experts in machine learning may be mildly disappointed with the lack of coverage some of their pet topics get. For example, while the chapter on graphical models is excellent as far as it goes, it only mentions the problem of learning graphical model structures (one of my areas of interest) in passing. Reinforcement learning (another personal area of interest) is discussed briefly in the introduction and then written off as beyond the scope of the book. However, the book is already a fabulous resource as it stands; complaining there's not even more of it would be gauche. The cover may look like goat barf, and there are some innocuous missing words here and there (hey, it's a first edition), but if you're serious about machine learning and not afraid of a little math, you should definitely own this book. I can only imagine how much cooler my own thesis research might have been if this book had been around a few years earlier. ## Frequently Bought Together - Pattern Recognition and Machine Learning (Information Science and Statistics) - Deep Learning (Adaptive Computation and Machine Learning series) - Deep Learning: Foundations and Concepts --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.nz/products/2214052-pattern-recognition-and-machine-learning-information-science-and-statistics](https://www.desertcart.nz/products/2214052-pattern-recognition-and-machine-learning-information-science-and-statistics) --- *Product available on Desertcart New Zealand* *Store origin: NZ* *Last updated: 2026-05-25*