This repository contains presentation materials and example code based on the book The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). It provides slides summarizing key concepts covered in the book and example code demonstrating practical applications.
The Elements of Statistical Learning
Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Publisher: Springer, 2nd Edition
The materials in this repository are organized by chapter, with each chapter including:
- Presentation Slides: A concise summary of key concepts for quick review and understanding.
- Example Code: Practical code examples that illustrate the statistical methods and algorithms discussed in the book.
The folders are named according to the content of each chapter:
Chapter02_Overview of Supervised Learning- Overview of supervised learning concepts, basic terminology, and models.Chapter04_Linear Classification- Linear classifiers, logistic regression, and their applications.Chapter05_Basis Expansion- Basis expansion in linear models.Chapter06_Kernel Smoothing- Smoothing and density estimation using kernel methods.Chapter07_Model Selection- Model selection and validation techniques.Chapter08_Model Inference and Averaging- Inference, model averaging, and regularization methods.Chapter09_Additive Models and Trees- Additive models, decision trees, and ensemble methods.Chapter10_Boosting- A deep dive into boosting algorithms.Chapter11_Neural Network- Overview and implementation of neural networks.Chapter12_SVM and PDA- Support Vector Machines (SVM) and Penalized Discriminant Analysis (PDA).