---
product_id: 51474023
title: "Pattern Recognition and Machine Learning (Information Science and Statistics)"
price: "€ 1197.55"
currency: EUR
in_stock: true
reviews_count: 13
url: https://www.desertcart.fi/products/51474023-pattern-recognition-and-machine-learning-information-science-and-statistics
store_origin: FI
region: Finland
---

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

**Price:** € 1197.55
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- **What is this?** Pattern Recognition and Machine Learning (Information Science and Statistics)
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## Description

Buy Pattern Recognition and Machine Learning (Information Science and Statistics) Newer (Colored) by Bishop, Christopher M. (ISBN: 9780387310732) from desertcart's Book Store. Everyday low prices and free delivery on eligible orders.

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.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | 146,562 in Books ( See Top 100 in Books ) 136 in Higher Education of Engineering 157 in Higher Mathematical Education 293 in Software Design & Development |
| Customer reviews | 4.6 4.6 out of 5 stars (744) |
| Dimensions  | 19.56 x 3.3 x 25.91 cm |
| Edition  | Newer (Colored) |
| ISBN-10  | 0387310738 |
| ISBN-13  | 978-0387310732 |
| Item weight  | 1.05 kg |
| Language  | English |
| Print length  | 798 pages |
| Publication date  | 1 Feb. 2007 |
| Publisher  | Springer |

## 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

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*Product available on Desertcart Finland*
*Store origin: FI*
*Last updated: 2026-03-17*