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Reviewer: Shrisha Rao

It is well known that the amount of data available and needing to be processed is increasing worldwide as a result of the widespread use of networked computers in all aspects of society and also the coming of age of new technologies such as the Internet of Things (IoT). A single computing device today possibly has more data storage capacity than all of the computers at a university did a few decades ago, and a person can comfortably carry in a personal device such as a smartphone enough data to fill all of the printed volumes in the Library of Congress. In this situation, it is also readily apparent that it is no longer possible to work out by hand all the ramifications that may be inherent in a large dataset. Algorithmic solutions must be found to questions like: Is a purchaser of a certain item in an online store also likely to be interested in some other item__?__ What do the data show about the efficacy of certain public policies__?__ If a government were to reduce the income tax and increase the sales tax on certain goods, would this increase the GDP or the net revenue__?__ How can law enforcement agencies find the hidden behavioral patterns unique to individuals who may be part of sleeper cells of terrorist organizations__?__ The field of machine learning finds its application in precisely this context. Built out of a judicious mix of statistics and algorithms (with appropriate use of discrete mathematics, linear algebra, operations research, and so on, added where needed), it enables us to find ways to gain knowledge from massive datasets that cannot be made sense of otherwise. This book is the third edition of a well-received work that does a good job of covering, in a single volume, a sampling of the spread of machine learning techniques in a manner suitable for a beginning student (who may be an advanced undergraduate or an early graduate student). The book is also usable by a practitioner who may wish to understand and use some specific machine learning techniques. In 19 chapters, including one of general introduction, the author covers the basics of important machine learning techniques such as supervised learning, reinforcement learning, multivariate methods, clustering, dimensionality reduction, hidden Markov models, and so on. An appendix gives some relevant background in probability theory, and the exercises at the end of each chapter are a welcome resource for instructors and students alike. However, as the focus of the book is on the mathematical concepts rather than on programming and actually working with datasets, practitioners or academics who wish to do hands-on work using machine learning will probably have to find other resources to help with such endeavors. Some theoreticians and practitioners may also find it useful to blend their study of machine learning with some exposure to classical statistics-based approaches, such as those in the books by Spirtes et al. [1] and Pearl [2]. Online Computing Reviews Service

Reviewer: Luca Longo

Machine learning is probably one of the fastest growing fields in computer science, with data sets getting larger and becoming available to a wider audience. This shift is reflected in the number of techniques and approaches devoted to transforming this big data into more organized and comprehensible knowledge. As the title truly reflects, this third edition is a good introduction to the field. It is an accessible book for graduate and postgraduate students, as well as self-learners facing machine learning for the first time. The volume opens with a gentle description of machine learning, including basic examples related to learning associations, classification, regression, and unsupervised and reinforcement learning. It then expands on these approaches while always maintaining a clear and easy-to-follow tone, without digging too much into complexity. The book is fundamentally a survey of approaches for machine learning; modern and innovative learning techniques are not covered. The strength of the volume is that it is probably one of the most accessible books for novices, and it contains reasonable content for those students and researchers willing to apply learning algorithms. It clearly sets out the differences between parametric and nonparametric models, providing a description of techniques for dimensionality reduction, supervised and unsupervised classification, and Bayesian approaches for learning. The author nicely ties together a wide range of topics in a way that allows readers to achieve a deeper understanding of the material. One of the book's weaknesses is that it could have been more case study oriented, with more practical examples and solutions to help novices and self-learners grasp the threshold notions behind machine learning. Another limitation is that the book merely covers the design and analysis of machine learning experiments, providing the reader with very little information on how to evaluate the performance of learning models. Overall, this book is easy to follow and very well organized, and it covers a wide range of topics with systematic discussions of several areas in machine learning. While the low level of detail might be useful for novices, experts and scholars will not benefit as much from this book. In a nutshell, the volume is an optimal resource for learners eager to use machine learning algorithms for classification or regression; however, self-learners should complement their studies with additional books focused more on case studies and practical applications. Online Computing Reviews Service