All Books

“The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)” is a comprehensive mathematical treatment of machine learning from a statistical perspective. Trevor Hastie, Robert Tibshirani and Jerome Friedman are the authors of this book. 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. 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. 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 practical algorithms, illustrated with numerous examples.

The “An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)” provides a fantastic introduction to the field. Gareth James, Daniela Witten, Trevor Hastie are the authors of this book. This book provides an excellent presentation and a very good compromise between statistics and data analysis. These experts come right and say which methods work best under which circumstances. While there are many fancy algorithms covered in the book, they highlight the advantages of the simpler ones. Emphasis on subjects that are not heavily addressed in most ML books.