Friedman, Jerome

last modified on Mon, 05 Sep 2022

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Jan 13, 2017	»	[ToDo] The Elements of Statistical Learning 1 min; updated Sep 5, 2022 Introduction Overview of Supervised Learning Linear Methods for Regression Linear Methods for Classification Basic Expansions and Regularization Kernel Smoothing Methods Model Assessment and Selection Model Inference and Averaging Additive Models, Trees, and Related Methods Boosting and Additive Trees Neural Networks Support Vector Machines and Flexible Discriminants Prototype Methods and Nearest-Neighbors Unsupervised Learning Random Forests Ensemble Learning Undirected Graphical Models High-Dimensional Problems: \(p » N\) The Elements of Statistical Learning: Data Mining, Inference and Prediction. Trevor Hastie; Robert Tibshirani; Jerome Friedman. web.stanford.edu . Jan 13, 2017.