Introduction to statistical learning : with applications in R

By:

James, Gareth

Contributor(s):

Series: Springer texts in StatisticsPublication details: Springer 2022 New YorkEdition: 2nd edDescription: 607pISBN:

9781071614204

Subject(s):

DDC classification:

510.5 JAM

Summary: This is a comprehensive textbook for the essential tools of modern statistical learning. It provides an accessible overview of the field and the most important modeling and prediction techniques. This book covers topics such as linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, 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.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Jammu General Stacks	Non-fiction	510.5 JAM (Browse shelf(Opens below))	Available		IIMJ-6861

Total holds: 0

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Previous	510.1 BAC Philosophical introduction to higher - order logics	510.1 HAM Lectures on the philosophy of mathematics	510.24 NIE Essential Math for Data Science : take control of your data with fundamental linear algebra, probability, and statistics	510.5 JAM Introduction to statistical learning : with applications in R	510.9 BRE Calculus reordered: a history of the big ideas	510.92 VEN Ramanujan: from zero to infinity	510.954 MAT Mathematics in ancient Jaina literature	Next

Table of Contents: 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

This is a comprehensive textbook for the essential tools of modern statistical learning. It provides an accessible overview of the field and the most important modeling and prediction techniques. This book covers topics such as linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, 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.

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