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
A First Encounter with Machine Learningby 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.
(15724 views)
An Introduction to Statistical Learningby G. James, D. Witten, T. Hastie, R. Tibshirani - Springer
This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.
(12670 views)
A Survey of Statistical Network Modelsby A. Goldenberg, A.X. Zheng, S.E. Fienberg, E.M. Airoldi - arXiv
We begin with the historical development of statistical network modeling and then we introduce some examples in the network literature. Our subsequent discussion focuses on prominent static and dynamic network models and their interconnections.
(11249 views)
Elements of Causal Inference: Foundations and Learning Algorithmsby J. Peters, D. Janzing, B. Schölkopf - The MIT Press
This book offers a self-contained and concise introduction to causal models and how to learn them from data. The book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from data ...
(11847 views)