Rating: 
Summary: One of the Essential Books on Modern Machine Learning
Review: This book is a miracle of clarity and comprehensiveness. It presents a unified approach to state of the art machine learning techniques from a statistical perspective. The layout is logical and the level of math is appropriate for applications-oriented engineers and computer scientists, as well as theorists. Sections where the book does need to go into heavier mathematics are clearly marked and generally optional. I found the book very easy to read, but at the same time very comprehensive.The book provides a very illuminating counterpoint to other books that promote the Computational Learning Theory (COLT / kernels / large margins) viewpoint of modern machine learning. Many of the same techniques such as boosting and support vector machines are discussed, but are motivated in different ways. Appropriate regularization is seen as the key to avoiding overfitting with complex models, rather than margin maximization. Mathematically you often end up solving the same problem, but personally I usually find the statistical approach much more direct and intuitive. This book is a nice follow on to introductory pattern recognition texts such as Duda and Hart, though it can be read without any prior pattern recognition knowledge. It provides a nice mix of theory and paractical algorithms, illustrated with numerous examples. An essential element of your machine learning library!
Rating:
Summary: Excellent introduction to statistical learning
Review: This book is an excellent survey of the huge area of statistics / computer science called statistical learning. The discussion is interesting and accurate, but not too theoretical. It is the best book to date for a general audience with a reasonable math/stat background. One of the strengths is the wide variety of topics covered; it is very comprehensive. If there is a weakness, it is that depth is limited. Plenty of references are provided for further study, and the authors maintain a website. Recommended as a reference or a starting point for an applied statistician or mathematician, or as a text for a first course in the subject.
Rating: 
Summary: Not recommended as a learning text
Review: This is not an introduction to statistical learning theory. It is a collection of overviews of various statistical methods presented rather than explained to the reader. In order to benefit from this book the reader should have a good background in matrix algebra and should already have a theoretical and working knowledge of the topics covered. For detail on the methods and their real world application the reader should also be prepared to consult other references. Two stars because, fairly or not, it does not have the pedagogical value that I expected of it.
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