Course Description ``Statistical learning'' refers to analysis of data with the objective of identifying patterns or trends. Chapter 4: Classification- pdf (part 1, part 2), ppt (part 1, part 2) Chapter 5: Resampling Methods- pdf, ppt. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. Chapter 6: Linear Model Selection and Regularization- pdf, ppt. Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. While the approach is statistical, the emphasis is on concepts rather than mathematics. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. 3.1 Introduction ; 3.2 Linear Regression Models and Least Squares ; 3.3 Multiple Regression from Simple Univariate Regression Many examples are given, with a liberal use of color graphics. While the approach is statistical, the emphasis is on concepts rather than mathematics. We distinguish supervised learning, in which we seek to predict an outcome measure or class based on a sample of input measures, from unsupervised learning, in which we seek to identify and describe relationships and patterns among a sample of input measures. Statistical learning theory deals with the problem of finding a predictive function based on data. Introduction. Documents for the elements of statistical learning. pdfs / The Elements of Statistical Learning - Data Mining, Inference and Prediction - 2nd Edition (ESLII_print4).pdf Go to file Go to file T; Go to line L; Copy path tpn Fix permissions. Available in PDF, DOC, XLS and PPT format. Title: Chapter 3: Linear Methods for Regression The Elements of Statistical Learning Aaron Smalter 1 Chapter 3Linear Methods for RegressionThe Elements of Statistical LearningAaron Smalter 2 Chapter Outline. Latest commit d93b294 Jan 16, 2016 History. The-Elements-Of-Statistical-Learning All the work is dedicated to the book writers from whom I learned a great deal: Mr. Robert Tibshirani, Mr. Trevor Hastie, Mr. Jerome Friedman. Chapter 7: Moving Beyond Linearity Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, and bioinformatics. Elements of Statistical Learning • 2.4 Statistical Decision Theory • 2.5 Local Methods in High Dimensions • 2.6 Statistical Models, Supervised Learning and Function Approximation • 2.7 Structured Regression Models • 2.8 Classes of Restricted Estimators • 2.9 Model Selection and the Bias–Variance Tradeoff. This repository contains R code for exercices and plots in the famous book. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. Many examples are given, with a liberal use of color graphics. Chapter 2: Statistical Learning- pdf (part 1, part 2), ppt (part 1, part 2) Chapter 3: Linear Regression- pdf, ppt.