--- product_id: 51474023 title: "Pattern Recognition and Machine Learning (Information Science and Statistics)" price: "Rp4278105" currency: IDR in_stock: true reviews_count: 13 url: https://www.desertcart.id/products/51474023-pattern-recognition-and-machine-learning-information-science-and-statistics store_origin: ID region: Indonesia --- # Pattern Recognition and Machine Learning (Information Science and Statistics) **Price:** Rp4278105 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Pattern Recognition and Machine Learning (Information Science and Statistics) - **How much does it cost?** Rp4278105 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.id](https://www.desertcart.id/products/51474023-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 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 pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts 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 wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine 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. Review: Superb - There are a huge number of machine learning books now available. I own many of them. But I don't think any have had such an impact as Chris Bishop's effort here - I certainly count it as my favourite. The material covered is not exhaustive (although good for 2006), but it's a good springboard to many other advanced texts. (The moniker of ML 'Bible' has apparently been passed to Kevin Murphy's book.) What *is* covered is explained with exceptional clarity with an eye for understanding the intuition as well as the theory. If you are after a practitioners guide, or a first ML book for self study, this probably isn't ideal. It assumes significant familiarity with multivariate calculus, probability and basic stats (identities, moments, regression, MLE etc.). The pitch is probably early post-graduate level, but with a few stretching parts. If this is your background, I think it's a better first ML book than MacKay (Information Theory ...), Murphy (Machine Learning ...), or Hastie et al. (Elements of Statistical Learning), due to its coherence of topics and consistency of depth. But those books are all excellent in their own ways. However, Barber (Bayesian Reasoning ...) is a good alternative. Most chapters are fairly self contained, so once you've worked your way through the first couple of chapters, you can skip around as required. A particular highlight for me were the chapters on EM and variational methods (ch 9 & 10); I think you'd be hard pressed to find a better explanation of either of them. Finally, worth pointing out it's unrepentantly Bayesian, and lacking some subtelty which may be grating for seasoned statisticians. Nevertheless, if the above sounds like what you're looking for, this is probably a good choice. Review: a great book, money well spent - This is a great book with one of the most clear presentations of several fundamental algorithms. In my experience this is a book I keep coming back to. ## Features - New Store Stock ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | 49,921 in Books ( See Top 100 in Books ) 35 in Higher Mathematical Education 45 in Higher Education of Engineering 67 in Software Design & Development | | Customer Reviews | 4.6 out of 5 stars 753 Reviews | ## Images ![Pattern Recognition and Machine Learning (Information Science and Statistics) - Image 1](https://m.media-amazon.com/images/I/71fqxXDY2ZL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Superb *by A***X on 27 May 2015* There are a huge number of machine learning books now available. I own many of them. But I don't think any have had such an impact as Chris Bishop's effort here - I certainly count it as my favourite. The material covered is not exhaustive (although good for 2006), but it's a good springboard to many other advanced texts. (The moniker of ML 'Bible' has apparently been passed to Kevin Murphy's book.) What *is* covered is explained with exceptional clarity with an eye for understanding the intuition as well as the theory. If you are after a practitioners guide, or a first ML book for self study, this probably isn't ideal. It assumes significant familiarity with multivariate calculus, probability and basic stats (identities, moments, regression, MLE etc.). The pitch is probably early post-graduate level, but with a few stretching parts. If this is your background, I think it's a better first ML book than MacKay (Information Theory ...), Murphy (Machine Learning ...), or Hastie et al. (Elements of Statistical Learning), due to its coherence of topics and consistency of depth. But those books are all excellent in their own ways. However, Barber (Bayesian Reasoning ...) is a good alternative. Most chapters are fairly self contained, so once you've worked your way through the first couple of chapters, you can skip around as required. A particular highlight for me were the chapters on EM and variational methods (ch 9 & 10); I think you'd be hard pressed to find a better explanation of either of them. Finally, worth pointing out it's unrepentantly Bayesian, and lacking some subtelty which may be grating for seasoned statisticians. Nevertheless, if the above sounds like what you're looking for, this is probably a good choice. ### ⭐⭐⭐⭐⭐ a great book, money well spent *by E***6 on 28 February 2020* This is a great book with one of the most clear presentations of several fundamental algorithms. In my experience this is a book I keep coming back to. ### ⭐⭐⭐⭐⭐ Excellent book *by C***S on 17 March 2019* It's one of the best if not the best book for theory in machine learning. It's readable and very comprehensible for someone who has a mathematical background. ## 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.id/products/51474023-pattern-recognition-and-machine-learning-information-science-and-statistics](https://www.desertcart.id/products/51474023-pattern-recognition-and-machine-learning-information-science-and-statistics) --- *Product available on Desertcart Indonesia* *Store origin: ID* *Last updated: 2026-05-27*