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Machine Learning
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Table of contents
Welcome
Introduction
Supervised Learning
Regression
1
Linear Regression
Practical Work 1
2
Multiple Linear Regression
PW 2
Classification
3
Logistic Regression
PW 3
4
Discriminant Analysis
PW 4
5
Decision Trees & Random Forests
PW 5
Dimensionality Reduction
6
Principal Components Analysis
PW 6
Unsupervised Learning
7
Kmeans & Hierarchical Clustering
PW 7
8
Gaussian Mixture Models & EM
PW 8
Hackathon
Hackathon
Appendix
A
Final Grades
B
Introduction to RStudio
C
Review on hypothesis testing
D
Use of qualitative predictors
E
Model Selection
F
References and Credits
G
Other References
Main References & Credits
E
Model Selection
Linear Model Selection and Best Subset Selection
Forward Stepwise Selection
Backward Stepwise Selection
Estimating Test Error Using Mallow’s Cp, AIC, BIC, Adjusted R-squared
Estimating Test Error Using Cross-Validation
Examples
Best Subset Selection
Forward Stepwise Selection and Model Selection Using Validation Set
Model Selection Using Cross-Validation
D
Use of qualitative predictors
F
References and Credits
On this page
E
Model Selection
Linear Model Selection and Best Subset Selection
Forward Stepwise Selection
Backward Stepwise Selection
Estimating Test Error Using Mallow’s Cp, AIC, BIC, Adjusted R-squared
Estimating Test Error Using Cross-Validation
Examples
Best Subset Selection
Forward Stepwise Selection and Model Selection Using Validation Set
Model Selection Using Cross-Validation