传统的机器学习教材可以分为两类：一类适用于具有充分数学知识的高年级本科生或研究生，另一类则是关于如何编写算法程序的入门手册。本书的特别之处是书中既展示了如何去使用构成机器学习方法的算法，也提供了理解这些算法如何工作以及为什么工作所需的数学背景。这使得本书自2009年首版以来大获好评并被国际上很多大学选用为本科生机器学习课程教材。2009年以后，机器学习领域又出现了一些显著的发展，比如机器学习算法的统计解释越来越有用并流行。而这给那些缺乏强大统计背景的计算机科学专业的学生带去学习上的困难。本书的第2版致力于弥补这一缺陷，帮助学生一方面通过掌握相关的数学和统计学知识，另一方面通过必要的编程和实践，来充分理解机器学习的现代算法。第2版中不但新增了深度信念网络和高斯过程两章，全书的章节都进行了重新的组织，使学习流程更为自然。书中增加了随机森林、感知机收敛定理、多层感知器共轭梯度优化等新内容，对支持向量机部分也进行了大幅的修改，还讨论了卡尔曼滤波器和粒子滤波器。本书既可用于一学期的机器学习入门课程，也可组织材料用于更高阶的课程。书中每章都包含详细的示例并提供进一步的阅读文献和问题。强烈鼓励学生使用代码进行练习，用于创建示例的所有代码都可以在作者的网站上找到。
★编辑推荐：
本书有两大特点使其成为国际上非常流行的机器学习教材。
1.实例来支持理论。本书涵盖了神经网络、图模型、强化学习、进化算法、降维方法及优化等机器学习重要方向。作者在保持学术严谨性和大量堆砌数学公式之间找到了完美的平衡，书中使用基于广泛可用的数据集的实例（并提供Python的代码）来充分展示理论，同时给学有余力的读者指出可在哪找到进一步深入学习的材料用于自学。
2.广泛触及各种学科和应用。机器学习的多学科性因其适用于金融、生物学、医学、物理、化学和工程学等领域而得到强调。作者从各种学科中选择实例，并以易于理解的风格编写，弥合了学科之间的鸿沟，实现了理论与实践的理想融合。
★媒体推荐/名人推荐/读者推荐：
“I have been using this textbook for an undergraduate machine learning class for several years. Some of the best features of this book are the inclusion of Python code in the text (not just on a website), explanation of what the code does, and, in some cases, partial numerical run-throughs of the code. This helps students understand the algorithms better than high-level descriptions and equations alone and eliminates many sources of ambiguity and misunderstanding.”
—Daniel Kifer, Pennsylvania State University
“This book will equip and engage students with its well-organised and -presented material. In each chapter, they will find thorough explanations, figures illustrating the discussed concepts and techniques, lots of programming (Python) and worked examples, practice questions, further readings, and a support website. The book will also be useful to professionals who can quickly inform and refresh their memory and knowledge of how machine learning works and what are the fundamental approaches and methods used in this area. As a whole, it provides an essential source for machine learning methodologies and techniques, how they work, and what are their application areas.”
—Ivan Jordanov, University of Portsmouth
“The book's emphasis on algorithms distinguishes it from other books on machine learning (ML). This is further highlighted by the extensive use of Python code to implement the algorithms. ... The topics chosen do reflect the current research areas in ML, and the book can be recommended to those wishing to gain an understanding of the current state of the field.”
—J. P. E. Hodgson, Saint Joseph's University, in Computing Reviews
“… liberally illustrated with many programming examples, using Python. It includes a basic primer on Python and has an accompanying website. It has excellent breadth and is comprehensive in terms of the topics it covers, both in terms of methods and in terms of concepts and theory. … I think the author has succeeded in his aim: the book provides an accessible introduction to machine learning. It would be excellent as a first exposure to the subject, and would put the various ideas in context …”
—David J. Hand, Imperial College London, in International Statistical Review
“I thought the first edition was hands down, one of the best texts covering applied machine learning from a Python perspective. I still consider this to be the case. The text, already extremely broad in scope, has been expanded to cover some very relevant modern topics … I highly recommend this text to anyone who wants to learn machine learning … I particularly recommend it to those students who have followed along from more of a statistical learning perspective (Ng, Hastie, Tibshirani) and are looking to broaden their knowledge of applications. The updated text is very timely, covering topics that are very popular right now and have little coverage in existing texts in this area.”
—Intelligent Trading Technology
“If you are interested in learning enough AI to understand the sort of new techniques being introduced into Web 2 applications, then this is a good place to start. … it covers the subject matter of many an introductory course on AI and it has references to the source material and further reading but it is written in a fairly casual style. Overall it works and much of the mathematics is explained in ways that make it fairly clear what is going on … . This is a suitable introduction to AI if you are studying the subject on your own and it would make a good course text for an introduction and overview of AI.”
—I-Programmer
还没人写过短评呢