ML Note
Introduction
Chapter1 Overview
Notations
Chapter2 Statistical Learning
What is Statistical Learning
Assessing Model Accuracy
Exercise
Chapter3 Overview of Supervised Learning
Variable Types and Terminology
Two Simple Approaches to Prediction
Statistical Decision Theory
Local Methods in High Dimensions
Statistical Models, Supervise Learning and Function Approximation
Structured Regression Models
Chapter4 Linear Regression
Linear Regression Models and Least Squares
Subset Selection
Shrinkage Methods
Methods Using Derived Input Directions
Discussion: A Comparison of the Selection and Shrinkage Methods
More on the Lasso and Related Path Algorithm
Reference
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Introduction
ESL Study Notes
This is a study note for ESL.
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