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1. AN INTRODUCTION TO STATISTICAL LEARNING UIC PDF
2. AN INTRODUCTION TO STATISTICAL LEARNING UIC SOFTWARE
3. AN INTRODUCTION TO STATISTICAL LEARNING UIC CODE

The second edition has been expanded to include the following topics of note:

AN INTRODUCTION TO STATISTICAL LEARNING UIC PDF

While the original has been around since 2013, the second edition was published very recently, and is now freely-available via PDF on the book’s website.Ī description, directly from the books’ website:Īs the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. The book, a staple of statistical learning texts, is accessible to readers of all levels, and can be read without much of an existing foundational knowledge in the area.

AN INTRODUCTION TO STATISTICAL LEARNING UIC CODE

R code has been updated throughout to ensure compatibility.An Introduction to Statistical Learning, with Applications in R, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, is an absolute classic in the space. 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.

The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. Two of the authors co-wrote 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 UIC SOFTWARE

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. Color graphics and real-world examples are used to illustrate the methods presented.

Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Summary: An Introduction to Statistical Learning 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. Series: Springer Texts in Statistics, 2197-4136 Contents: Preface - 1 Introduction - 2 Statistical Learning - 3 Linear Regression - 4 Classification - 5 Resampling Methods - 6 Linear Model Selection and Regularization - 7 Moving Beyond Linearity - 8 Tree-Based Methods - 9 Support Vector Machines - 10 Deep Learning - 11 Survival Analysis and Censored Data - 12 Unsupervised Learning - 13 Multiple Testing - Index.