FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title: An Introduction to Statistical Learning: with Applications in R, 2nd Edition
 Author(s) Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
 Publisher: Springer; 2nd ed. (2021); eBook (Corrected Edition, June 21, 2023)
 Hardcover: 622 pages
 eBook: PDF (615 pages)
 Language: English
 ISBN10: 1071614177
 ISBN13: 9781071614174
 Share This:
Book Description
This book provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, treebased methods, support vector machines, clustering, and more. Color graphics and realworld examples are used to illustrate the methods presented.
Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
Two of the authors cowrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience.
This book is targeted at statisticians and nonstatisticians alike who wish to use cuttingedge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
About the Authors Gareth James is the E. Morgan Stanley Chair in Business Administration and a professor of data sciences and operations at the Marshall School of Business at the University of Southern California.
 Daniela Witten is a professor of statistics and biostatistics at the University of Washington.
 Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are coauthors of the successful textbook Elements of Statistical Learning.
 Machine Learning
 The R Programming Language
 Statistics, Mathematical Statistics, and SAS Programming
 Data Analysis and Data Mining
 Artificial Intelligence
 An Introduction to Statistical Learning: with Applications in R (Gareth James, et al.)
 The Mirror Site (1)  PDF (1st Edition)
 The Mirror Site (2)  PDF (1st Edition)
 Lecture Slides, Videos, Interviews, etc.
 Book Homepage (R and Python Editions, Errata, Resources, etc.)

Introduction to Statistical Learning: with Applications in Python
This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

The Elements of Statistical Learning: Data Mining, Inference, etc.
This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics.

Statistical Learning and Sequential Prediction
This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction, a unified approach to analyzing learning in both scenarios, brings together ideas from probability and statistics, game theory, algorithms, and optimization.

Statistical Foundations of Machine Learning: The Handbook
This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.

Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudocode for the most important algorithms.

Bayesian Methods for Hackers: Probabilistic Programming
This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation  no advanced mathematics required.

Pattern Recognition and Machine Learning (Christopher Bishop)
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

Foundations of Machine Learning (Mehryar Mohri, et al)
This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

The HundredPage Machine Learning Book (Andriy Burkov)
Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.
:






















