Pattern Recognition and Machine Learning-01-Preface

Preface

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 propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.

模式识别起源于工程学，机器学习起源于计算机科学。但它们可以认为是相同领域的不同层面，它们在近十年里经历了重大的发展。特别是，贝叶斯方法从专家的专属变成了主流，图形模式也已经成为描述和应用概率模型的一种基本框架。同时，由于一系列近似推理算法的发展，例如变分贝叶斯和期望繁殖，使得贝叶斯方法的实用性大大增加。类似的，那些基于核的新模型也在算法和应用上产生了巨大影响。

This new textbook reflects 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 well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine 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.

本书介绍了这些最近的发展，并提供了模式识别和机器学习领域的全面介绍。本书适合高年级本科生、硕士生或一年级博士研究生，以及其他该领域的研究者，并假设读者之前没有接触过模式识别和机器学习的概念。本书的阅读者需要具备多元微积分和基本线性代数的知识，对概率的了解也会帮助阅读本书，但是没有这些也没关系，因为本书包含了可以自学的基本概率理论的介绍。

Because this book has broad scope, it is impossible to provide a complete list of references, and in particular no attempt has been made to provide accurate historical attribution of ideas.  Instead, the aim has been to give references that offer greater detail than is possible here and that hopefully provide entry points into what, in some cases, is a very extensive literature. For this reason, the references are often to more recent textbooks and review articles rather than to original sources.

因为本书包含太广，所以不能提供一个完整的参考列表，尤其是没有提供准确的思想历史归属的想法。相反，本书主要是提供拥有更多细节的参考文献而不是最初提出理论或者出发点的文献。由于这个原因，参考文献通常是最近的书籍和文章而不是原始的出处。

The book is supported by a great deal of additional material, including lecture slides as well as the complete set of figures used in the book, and the reader is encouraged to visit the book web site for the latest information:

本书还提供了许多额外的资源，包括幻灯片和本书使用的完整的图片集，希望读者访问本书的网站来获取最新的消息。

http://research.microsoft.com/∼cmbishop/PRML

Exercises

The exercises that appear at the end of every chapter form an important component of the book. Each exercise has been carefully chosen to reinforce concepts explained in the text or to develop and generalize them in significant ways, and each is graded according to difficulty ranging from (*), which denotes a simple exercise taking a few minutes to complete, through to (***), which denotes a significantly more complex exercise.

练习

每章结尾的练习题是本书是重要组成部分。每一个练习都是经过精心选择的，以此来增强对概念的理解，或者以一种有效的方式对概念进行推广。每一题都根据它的难度用（*）号来分级，（*）表示之花几分钟就能搞定的简单练习，（***）表明更复杂。

It has been difficult to know to what extent these solutions should be made widely available. Those engaged in self-study will find worked solutions very beneficial, whereas many course tutors request that solutions be available only via the publisher so that the exercises may be used in class.  In order to try to meet these conflicting requirements, those exercises that help amplify key points in the text, or that fill in important details, have solutions that are available as a PDF file from the book web site. Such exercises are denoted byWWW. Solutions for the remaining exercises are available to course tutors by contacting the publisher (contact details are given on the book web site). Readers are strongly encouraged to work through the exercises unaided, and to turn to the solutions only as required.

我们很难知道答案在多大程度内不同是可以接受的。那些喜欢自学的读者会发现提供答案是很有利的，但许多授课教师要求答案只能通过老师分发，那样这些练习就可在课堂上使用。为了满足这两个相互矛盾的要求，这些增强书中重点或者包含重点细节的练习，它们的答案可以在本书的网站上以PDF的形式得到，这样的练习标注了WWW。剩下的练习的答案，授课老师可以联系出版社得到。我们鼓励读者独立解答这些习题，在需要的时候才参考答案。

Although this book focuses on concepts and principles, in a taught course the students should ideally have the opportunity to experiment with some of the key algorithms using appropriate data sets. A companion volume (Bishop and Nabney, 2008) will deal with practical aspects of pattern recognition and machine learning, and will be accompanied by Matlab software implementing most of the algorithms discussed in this book.

虽然本书的重点在概念和规律，在课堂上同学们应该有机会利用合适的数据集做一些关键算法的试验。它的姊妹篇(Bishop and Nabney,2008)将会解决模式识别和机器学习的实践方面，带有本书中讨论的大多数算法的matlab实现。