--- product_id: 261744461 title: "Springer Pattern Recognition and Machine Learning" price: "1281494Fr" currency: CDF in_stock: true reviews_count: 5 url: https://congo.desertcart.com/products/261744461-springer-pattern-recognition-and-machine-learning store_origin: CD region: Congo --- # Bayesian inference algorithms Self-contained probability intro Graphical models for ML Springer Pattern Recognition and Machine Learning **Price:** 1281494Fr **Availability:** βœ… In Stock ## Summary > πŸ“ˆ Unlock the Bayesian edge in machine learning β€” where math meets mastery! ## Quick Answers - **What is this?** Springer Pattern Recognition and Machine Learning - **How much does it cost?** 1281494Fr with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [congo.desertcart.com](https://congo.desertcart.com/products/261744461-springer-pattern-recognition-and-machine-learning) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Key Features - β€’ **Accessible Yet Rigorous:** No prior ML knowledge neededβ€”just basic calculus and linear algebra to unlock advanced concepts. - β€’ **Exercises That Cement Mastery:** Engage with challenging, well-crafted problems that transform theory into expertise. - β€’ **Graphical Models as a Core Tool:** Explore machine learning through intuitive graphical models, a unique approach not found in other texts. - β€’ **Accelerated Approximate Inference:** Leverage fast algorithms that provide practical solutions when exact answers are out of reach. - β€’ **Master Bayesian Pattern Recognition:** Dive deep into the first textbook to fully embrace Bayesian methods in pattern recognition. ## Overview Springer's 'Pattern Recognition and Machine Learning' is a pioneering textbook that introduces Bayesian perspectives and graphical models to machine learning. Designed for professionals with a solid math foundation, it offers fast approximate inference algorithms and a self-contained introduction to probability. Highly rated and widely respected, this book is essential for those aiming to deeply understand and implement advanced ML methods beyond surface-level knowledge. ## 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: Sehr gutes Buch - Habe das Buch bestellt, alles super funktioniert. ## Features - Springer ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #140,599 in Books ( See Top 100 in Books ) #346 in Applied Mathematics #394 in Software Design, Testing & Engineering #978 in Computer Science | | Customer Reviews | 4.6 out of 5 stars 743 Reviews | ## Images ![Springer Pattern Recognition and Machine Learning - Image 1](https://m.media-amazon.com/images/I/71fqxXDY2ZL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Excellent text *by K***A on 22 February 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. ### ⭐⭐⭐⭐⭐ Sehr gutes Buch *by S***A on 24 March 2026* Habe das Buch bestellt, alles super funktioniert. ### ⭐⭐⭐⭐⭐ Amazingly written, fantastic print quality. *by P***L on 23 January 2023* This book is excellently written. It is not simply a reference bible, the author tells a chronological story and takes you along for the ride. The print quality of my copy is excellent, nice waxy paper, crisp text and nice and colourful. As you've probably read elsewhere online, you will need to have done prior courses in probability and linear algebra, as the introductory chapters here, although technically "self contained", are very dense. Although Kevin Murphy's new 2022 book is also great, it feels like more of a reference on a zillion topics. Whereas with PMRL, Bishop is really trying to get you to understand the fundamentals. ## 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? - πŸ›’ **Trusted by 1.3+ Million Shoppers** β€” Serving international shoppers since 2016 - 🌍 **Shop Globally** β€” Access 737+ million products across 21 categories - πŸ’° **No Hidden Fees** β€” All customs, duties, and taxes included in the price - πŸ”„ **15-Day Free Returns** β€” Hassle-free returns (30 days for PRO members) - πŸ”’ **Secure Payments** β€” Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** β€” Based on 8,000+ happy customer reviews **Shop now:** [https://congo.desertcart.com/products/261744461-springer-pattern-recognition-and-machine-learning](https://congo.desertcart.com/products/261744461-springer-pattern-recognition-and-machine-learning) --- *Product available on Desertcart Congo* *Store origin: CD* *Last updated: 2026-09-09*