The Elements of Statistical Learning: Data Mining, Inference, and Prediction
by T. Hastie, R. Tibshirani, J. Friedman
Publisher: Springer 2009
Number of pages: 764
This book is an attempt to bring together many of the important new ideas in learning, and explain them in a statistical framework. While some mathematical details are needed, the authors emphasize the methods and their conceptual underpinnings rather than their theoretical properties. This book will appeal not just to statisticians but also to researchers and practitioners in a wide variety of fields.
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by David Beyer - O'Reilly Media
The series of interviews in this exclusive report unpack concepts and innovations that represent the frontiers of ever-smarter machines. You’ll get a rare glimpse into this exciting field through the eyes of some of its leading minds.
by Shai Shalev-Shwartz, Shai Ben-David - Cambridge University Press
This book introduces machine learning and the algorithmic paradigms it offers. It provides a theoretical account of the fundamentals underlying machine learning and mathematical derivations that transform these principles into practical algorithms.
by Aaron Hertzmann - University of Toronto
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.
by Max Welling - University of California Irvine
The book you see before you is meant for those starting out in the field of machine learning, who need a simple, intuitive explanation of some of the most useful algorithms that our field has to offer. A prelude to the more advanced text books.