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Course: Statistical Learning; Stanford Online, Lagunita; (https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about)
Student: David Aaron Campos
Repository: https://github.com/dcamposliz/statLearning
Date: June 2016
Faculty: Trevor Hastie; Rob Tibshirani
Textbook: Introduction to Statistical Learning; (http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf)
About R: https://www.r-project.org/about.html
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CHAPTER 1 - INTRODUCTION
- Opening Remarks
- Examples and Framework
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CHAPTER 2 - OVERVIEW OF STATISTICAL LEARNING
- Introduction to Regression Models
- Dimensionality and Structured Models
- Model Selection and Bias-Variance Tradeoff
- Classification
- Introduction to R
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CHAPTER 3 - LINEAR REGRESSION
- Simple Linear Regression
- Hypothesis Testing and Confidence Intervals
- Multiple Linear Regression
- Some Important Questions
- Extensions of the Linear Model
- Linear Regression in R
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CHAPTER 4 - CLASSIFICATION
- Introduction to Classification Problems
- Logistic Regression
- Multivariate Logistic Regression
- Logistic Regression - Case-Control Sampling and Multiclass
- Discriminant Analysis
- Gaussian Discriminant Analysis
- Gaussian Discriminant Analysis - Many Variables
- Quadratic Discriminant Analysis and Naive Bayes
- Classification in R