The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, 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 techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely 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. 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.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

《人工智能:一种现代方法》(第2版中文版)以详尽和丰富的资料，从理性智能体的角度，全面阐述了人工智能领域的核心内容，并深入介绍了各个主要的研究方向，是一本难得的综合性教材。全书分为八大部分：第一部分“人工智能” ，第二部分“问题求解” ，第三部分“ 知识与推理” ，第四部分“规划” ，第五部分“不确定知识与推理” ，第六部分“学习” ，第七部分“通讯、感知与行动” ，第八部分“ 结论” 。

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Class-tested and coherent, this groundbreaking new textbook teaches classic web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.
Contents
1. Information retrieval using the Boolean model; 2. The dictionary and postings lists; 3. Tolerant retrieval; 4. Index construction; 5. Index compression; 6. Scoring and term weighting; 7. Vector space retrieval; 8. Evaluation in information retrieval; 9. Relevance feedback and query expansion; 10. XML retrieval; 11. Probabilistic information retrieval; 12. Language models for information retrieval; 13. Text classification and Naive Bayes; 14. Vector space classification; 15. Support vector machines and kernel functions; 16. Flat clustering; 17. Hierarchical clustering; 18. Dimensionality reduction and latent semantic indexing; 19. Web search basics; 20. Web crawling and indexes; 21. Link analysis.
Reviews
“This is the first book that gives you a complete picture of the complications that arise in building a modern web-scale search engine. You'll learn about ranking SVMs, XML, DNS, and LSI. You'll discover the seedy underworld of spam, cloaking, and doorway pages. You'll see how MapReduce and other approaches to parallelism allow us to go beyond megabytes and to efficiently manage petabytes." -Peter Norvig, Director of Research, Google Inc.
"Introduction to Information Retrieval is a comprehensive, up-to-date, and well-written introduction to an increasingly important and rapidly growing area of computer science. Finally, there is a high-quality textbook for an area that was desperately in need of one." -Raymond J. Mooney, Professor of Computer Sciences, University of Texas at Austin
“Through compelling exposition and choice of topics, the authors vividly convey both the fundamental ideas and the rapidly expanding reach of information retrieval as a field.” -Jon Kleinberg, Professor of Computer Science, Cornell University

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts. -Sanjay Ranka, University of Florida In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules). -Mohammed Zaki, Rensselaer Polytechnic Institute

《数学女孩》以小说的形式展开，重点描述一群年轻人探寻数学中的美。内容由浅入深，数学讲解部分十分精妙，被称为“绝赞的初等数学科普书”。内容涉及数列和数学模型、斐波那契数列、卷积、调和数、泰勒展开、巴塞尔问题、分拆数等，非常适合对数学感兴趣的初高中生以及成人阅读。

《模式分类》(原书第2版)的第1版《模式分类与场景分析》出版于1973年，是模式识别和场景分析领域奠基性的经曲名著。在第2版中，除了保留了第1版的关于统计模式识别和结构模式识别的主要内容以外，读者将会发现新增了许多近25年来的新理论和新方法，其中包括神经网络、机器学习、数据挖掘、进化计算、不变量理论、隐马尔可夫模型、统计学习理论和支持向量机等。作者还为未来25年的模式识别的发展指明了方向。书中包含许多实例，各种不同方法的对比，丰富的图表，以及大量的课后习题和计算机练习。

这是一本关于生物信息学算法和计算思想的导论性教科书，原著由国际上的权威学者撰写，经国内知名专家精心翻译为中文，系统介绍推动生物信息学不断进步的算法原理。全书强调的是算法中思想的运用，而不是对表面上并不相关的各类问题进行简单的堆砌。 体现了以下特色： 阐述生物学中的相关问题，涉及对问题的模型化处理并提供一种或多种解决方案： 简要介绍生物信息学领域领军人物； 饶有趣味的小插图使得概念更加具体和形象，方法更容易被领会，激励学生学习的兴趣并鼓励他们加入到生物信息学研究工作中来。 书中的大量论述表明：较少的几种设计思想就能解决大量的生物学难题。 目前，可供本科学生使用的生物信息学著作为数不多，本书恰恰是其中的一本。国内生物信息学，计算生物学、计算数学等领域的本科生、研究生和其他研究人员，会从书中汲取基本的算法原理、解决实际问题的方法和技巧，进而更好地从事相关研究工作。

本书介绍了统计学习理论和支持向量机的关键思想、结论和方法，以及该领域的最新进展。统计学习理论是针对小样本情况研究统计学习规律的理论，是传统统计学的重要发展和补充。其核心思想是通过控制学习机器的容量实现对推广能力的控制。由Springer-Verlag出版社授权出版。

This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts which are set in abstract settings.

《概率论及其应用》(第3版)涉及面极广，不仅讨论了概率论在离散空间中的诸多课题，也涉及了概率论在物理学、化学、生物学(特别是遗传学)、博弈论及经济学等方面的应用，主要内容有：样本空间及其上的概率计算，独立随机变量之和的随机起伏，事件的组合及条件概率，离散随机变量及其数字特征，大数定律，离散的马尔可夫过程及其各种重要特征，更新理论等，除正文外，《概率论及其应用》(第3版)还附有六七百道习题和大量的附录。

《回归分析》源于作者多年在密歇根大学教授回归分析的课程讲义，从基本的统计概念讲起，对线性回归分析的基本假定、回归中的统计推论和回归诊断做了详尽的介绍，同时还涵盖了很多在社会科学中对实际研究非常有用的内容，包括虚拟变量、交互作用、辅助回归、多项式回归、样条函数回归和阶跃函数回归等。此外，《回归分析》还涉及通径分析、纵贯数据模型、多层线性模型和Iogit模型等方面的内容。

科学计算中的蒙特卡罗策略，ISBN：9787040258370，作者：刘军 著，唐年胜，周勇，徐亮 译

封面图片为英国伯明翰塞尔福瑞吉百货大楼，其极具线条感的轮廓外型优美，犹如水波的流动。其外表悬挂了1.5万个铝碟，创造出一种极具现代气息的纹理装饰效果，有如夜空下水流的波光粼粼，闪烁于月光之下，使建筑的商业氛围表现到极致。设计该建筑的英国“未来系统建筑事物所”，将商场内部围合成一个顶部采光的中庭，配以交叉的自动扶梯，使购物环境呈现出一种凝聚的向心力和商业广告的展示效应。作为英国第二商业城市伯明翰的建筑地标，人们称该建筑为“未来的百货商店”。因其设计构思的前卫性，该建筑获得2004年英国皇家建筑学会的“建筑设计奖”和2004年“英国皇家工艺美术委员会奖”等多个奖项。
本书从计算机科学领域的角度出发，介绍了信息检索的基础知识，并对当前信息检索的发展做了回顾，重点介绍了搜索引擎的核心技术，如文档分类和文档聚类问题，以及机器学习和数值计算方法。书中所有重要的思想都用示例进行了解释，生动形象，引人入胜，实现了理论与实战的完美结合。
本书的三位作者均是信息检索领域的顶级专家，两位来自学术教育界，一位来自硅谷业界，使本书既具备深厚的理论基础，又代表了尖端科技水准。因此，该书甫一出版，即被奉为该领域的权威著作，备受瞩目。目前已被众多世界名校采用为信息检索课程的教材。

《统计自然语言处理基础：国外计算机科学教材系列》是一本全面系统地介绍统计自然语言处理技术的专著，被国内外许多所著名大学选为计算语言学相关课程的教材。《统计自然语言处理基础：国外计算机科学教材系列》涵盖的内容十分广泛，分为四个部分，共16章，包括了构建自然语言处理软件工具将用到的几乎所有理论和算法。全书的论述过程由浅入深，从数学基础到精确的理论算法，从简单的词法分析到复杂的语法分析，适合不同水平的读者群的需求。同时，《统计自然语言处理基础：国外计算机科学教材系列》将理论与实践紧密联系在一起，在介绍理论知识的基础上给出了自然语言处理技术的高层应用（如信息检索等）。在《统计自然语言处理基础：国外计算机科学教材系列》的配套网站上提供了许多相关资源和工具，便于读者结合书中习题，在实践中获得提高。近年来，自然语言处理中的统计学方法已经逐渐成为主流。

《概率论沉思录(英文版)》将概率和统计推断融合在一起，用新的观点生动地描述了概率论在物理学、数学、经济学、化学和生物学等领域中的广泛应用，尤其是它阐述了贝叶斯理论的丰富应用，弥补了其他概率和统计教材的不足。全书分为两大部分。第一部分包括10章内容，讲解抽样理论、假设检验、参数估计等概率论的原理及其初等应用；第二部分包括12章内容，讲解概率论的高级应用，如在物理测量、通信理论中的应用。《概率论沉思录(英文版)》还附有大量习题，内容全面，体例完整。
《概率论沉思录(英文版)》内容不局限于某一特定领域，适合涉及数据分析的各领域工作者阅读，也可作为高年级本科生和研究生相关课程的教材。

本书是英国剑桥大学卡文迪许实验室的著名学者David J．C．MacKay博士总结多年教学经验和科研成果，于2003年推出的一部力作。本书作者不仅透彻地论述了传统信息论的内容和最新编码算法，而且以高度的学科驾驭能力，匠心独具地在一个统一框架下讨论了贝叶斯数据建模、蒙特卡罗方法、聚类算法、神经网络等属于机器学习和推理领域的主题，从而很好地将诸多学科的技术内涵融会贯通。本书注重理论与实际的结合，内容组织科学严谨，反映了多门学科的内在联系和发展趋势。同时，本书还包含了丰富的例题和近400道习题(其中许多习题还配有详细的解答)，便于教学或自学，适合作为信息科学与技术相关专业高年级本科生和研究生教材，对相关专业技术人员也不失为一本有益的参考书。...