As 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. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.
The book has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian and Vietnamese.
The First Edition topics include:
- Sparse methods for classification and regression
- Decision trees
- Boosting
- Support vector machines
- Clustering
The Second Edition adds:
- Deep learning
- Survival analysis
- Multiple testing
- Naive Bayes and generalized linear models
- Bayesian additive regression trees
- Matrix completion
Libro: https://www.statlearning.com/
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