Description
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
The Elements of Statistical Learning is a foundational reference for modern statistical learning methods used in data science, machine learning and applied analytics. Written by three leading statisticians, the book presents a unified framework for modeling, prediction and inference across a wide range of real world problems.
The text focuses on statistical learning concepts rather than heavy mathematical derivations, so it works for readers with a solid quantitative background while staying rigorous enough for advanced study. Clear explanations and visual intuition show how data driven models are built, evaluated and interpreted in fields such as medicine, biology, finance, marketing and engineering.
Supervised and unsupervised methods
Coverage spans supervised learning techniques such as regression, classification, support vector machines, neural networks, decision trees and boosting, as well as unsupervised methods including clustering, dimensionality reduction, graphical models, spectral clustering and non-negative matrix factorization. Modern regularization methods such as the lasso and least angle regression are also covered.
High dimensional data and false discovery
The book addresses the challenges of high dimensional data, where the number of variables exceeds the number of observations, with practical discussions of multiple testing and false discovery rates.
Who reads this book
Richly illustrated with color graphics and real data examples, the book works as a graduate level textbook and as a long term reference for professionals who want to understand the principles behind widely used machine learning algorithms: not just how to apply them, but why they work.
Book details
- Title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- ISBN-13: 9780387848570
Questions buyers ask
Who is The Elements of Statistical Learning written for?
Readers with a solid quantitative background who want to understand statistical learning methods used in data science and machine learning, from graduate students to working professionals.
Does it require heavy mathematics to follow?
It focuses on the concepts behind statistical learning rather than heavy derivations, though it stays rigorous enough for advanced study.
Does it cover both supervised and unsupervised learning?
Yes, it covers supervised techniques like regression and neural networks alongside unsupervised methods like clustering and dimensionality reduction.
Is it useful outside computer science, for fields like medicine or finance?
Yes, the authors work through examples in medicine, biology, finance, marketing and engineering.
Does it cover high dimensional data problems?
Yes, it addresses cases where the number of variables exceeds the number of observations, including multiple testing and false discovery rates.
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