An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

An Introduction to Statistical Learning, Second Edition

An Introduction to Statistical Learning provides an accessible and practical introduction to statistical learning—the powerful collection of methods used to understand complex data, build predictive models, and extract meaningful insights. Designed for both statisticians and non-statisticians, this widely useful textbook explains important statistical learning techniques without requiring an advanced mathematical background.

The book covers essential modeling and prediction methods with applications across fields such as biology, finance, marketing, and astrophysics. Color graphics and real-world examples make complex concepts easier to understand, while step-by-step R tutorials help readers put the methods into practice using one of the most popular open-source statistical software platforms.

Comprehensive Coverage of Statistical Learning

Topics include:

• Linear regression

• Classification

• Resampling methods

• Shrinkage approaches

• Tree-based methods

• Support vector machines

• Clustering

• Deep learning

• Survival analysis

• Multiple testing

• Naïve Bayes

• Generalized linear models

• Bayesian additive regression trees

• Matrix completion

• Statistical modeling and prediction

What's New in the Second Edition?

The Second Edition significantly expands the original material with:

• New chapters on deep learning, survival analysis, and multiple testing

• Expanded coverage of naïve Bayes

• Expanded discussion of generalized linear models

• Additional material on Bayesian additive regression trees

• Expanded treatment of matrix completion

• Updated R code throughout for improved compatibility

• New examples and applications supporting modern statistical learning practice

Accessible Without Advanced Mathematics

One of the book's key strengths is its approachable presentation. It assumes only a previous course in linear regression and does not require prior knowledge of matrix algebra. This makes it suitable for readers who want to learn practical statistical learning techniques without beginning with an extensive theoretical or mathematical treatment.

Readers familiar with The Elements of Statistical Learning will recognize many related topics, but An Introduction to Statistical Learning presents them at a level intended for a much broader audience.

Learn Statistical Learning Through R

Each chapter includes a tutorial demonstrating how to implement the methods discussed using R. These practical tutorials help bridge the gap between statistical concepts and real-world data analysis, making the book especially useful for students, researchers, analysts, and practitioners working with data.

Ideal for: Students of statistics and data science, researchers, analysts, scientists, business professionals, machine learning practitioners, and anyone looking for an accessible introduction to modern statistical learning.

Build practical statistical learning skills with a clear, application-focused guide to modeling, prediction, data analysis, and machine learning techniques.

Product details