The Elements of Statistical Learning: Data Mining, Inference, and Prediction
by T. Hastie, R. Tibshirani, J. Friedman
Publisher: Springer 2009
ISBN/ASIN: 0387848576
ISBN-13: 9780387848570
Number of pages: 764
Description:
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.
Download or read it online for free here:
Download link
(8.2MB, PDF)
Similar books
Machine Learningby Abdelhamid Mellouk, Abdennacer Chebira - InTech
Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, and more.
(16202 views)
An Introduction to Probabilistic Programmingby Jan-Willem van de Meent, et al. - arXiv.org
This text is designed to be a graduate-level introduction to probabilistic programming. It provides a thorough background for anyone wishing to use a probabilistic programming system, and introduces the techniques needed to build these systems.
(4805 views)
Information Theory, Inference, and Learning Algorithmsby David J. C. MacKay - Cambridge University Press
A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.
(28733 views)
Algorithms for Reinforcement Learningby Csaba Szepesvari - Morgan and Claypool Publishers
We focus on those algorithms of reinforcement learning that build on the theory of dynamic programming. We give a comprehensive catalog of learning problems, describe the core ideas, followed by the discussion of their properties and limitations.
(7743 views)