This repository contains R code for exercices and plots in the famous book. Course Description ``Statistical learning'' refers to analysis of data with the objective of identifying patterns or trends. 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. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. Latest commit d93b294 Jan 16, 2016 History. While the approach is statistical, the emphasis is on concepts rather than mathematics. 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. 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. Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Many examples are given, with a liberal use of color graphics. Statistical learning theory deals with the problem of finding a predictive function based on data. 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. While the approach is statistical, the emphasis is on concepts rather than mathematics. Available in PDF, DOC, XLS and PPT format. Documents for the elements of statistical learning. Introduction. Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, and bioinformatics. 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. 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. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. Chapter 7: Moving Beyond Linearity It is a valuable resource for statisticians and anyone interested in data mining in science or industry. 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. 3.1 Introduction ; 3.2 Linear Regression Models and Least Squares ; 3.3 Multiple Regression from Simple Univariate Regression
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