Elements of Statistical Learning by Hastie, Tibshirani, Friedman
Elements of Statistical Learning - Table of Contents
1. Introduction
2. Overview of Supervised Learning
3. Linear Methods for Regression
4. Linear Methods for Classification
5. Basis Expansions and Regularization
6. Kernel Smoothing Methods
7. Model Assessment and Selection
8. Model Inference and Averaging
9. Additive Models, Trees, and Related Methods
10. Boosting and Additive Trees
11. Neural Networks
12. Support Vector Machines and Flexible Discriminants
13. Prototype Methods and Nearest-Neighbors
14. Unsupervised Learning
15. Random Forests
16. Ensemble Learning
17. Undirected Graphical Models
18. High-Dimensional Problems: p » N
What You Will Learn in Elements of Statistical Learning
"The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition" by Trevor Hastie, Robert Tibshirani and Jerome Friedman is a cornerstone in "statistical learning" and "machine learning". The book explains how to build predictive models using a wide range of techniques, from simple "regression" methods to advanced ensemble and nonparametric approaches. It balances theory with practical examples, making complex concepts clear for both students and practitioners.
The authors explore key methods like linear regression, logistic regression, decision trees, "support vector machines", and neural networks. They also cover ensemble techniques such as bagging, boosting, and random forests, providing intuition on why these methods improve prediction accuracy. Throughout, the book emphasizes the "bias-variance trade-off", overfitting, and model selection, offering a strong foundation for understanding the principles behind modern data science.
Beyond algorithms, the book includes real-world applications in finance, bioinformatics, and other data-intensive fields. It offers clear explanations of optimization techniques, probability theory, and practical implementation tips, making it a comprehensive guide for anyone serious about mastering "predictive modeling". Written by leading experts, it’s an essential reference for graduate students, researchers, and professionals seeking to deepen their understanding of data-driven decision-making.
Book Details & Specifications
Title:
Elements of Statistical Learning by Hastie, Tibshirani, Friedman
Publisher:
Springer
Year:
2008
Pages:
764
Type:
PDF
Language:
English
ISBN-10 #:
0387848576
ISBN-13 #:
978-0387848570
License:
External Educational Resource
Amazon:
Amazon
About the Author: Trevor Hastie, Robert Tibshirani and Jerome Friedman
The author
Trevor Hastie, Robert Tibshirani and Jerome Friedman
are leading experts in "statistical learning" and "data science". Hastie, born in South Africa, earned his Ph.D. from Stanford University and specializes in "non-parametric regression" and predictive modeling. Tibshirani, from Canada, is known for the "lasso" method and advanced regression techniques. Friedman, an American physicist-turned-statistician, contributed heavily to "machine learning" methods like CART and gradient boosting. Together, they authored "The Elements of Statistical Learning", a foundational work in "data analysis" and predictive modeling. Their research has shaped modern analytics, helping researchers and practitioners build robust, interpretable "models" for real-world applications.
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