“The Elements of Statistical Learning: Data Mining, Inference, and Prediction” is a comprehensive textbook on statistical learning and data mining, written by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The book is aimed at advanced undergraduate and graduate students in statistics, computer science, and related fields, as well as researchers and practitioners in industry.
The book covers a wide range of topics in statistical learning, including supervised learning, unsupervised learning, and semi-supervised learning. It provides a rigorous and detailed treatment of the mathematical foundations of statistical learning, including linear and nonlinear models, regularization methods, decision trees, ensemble methods, and neural network.
