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Preface
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Introduction
I Topic I: Linear Regression
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Placeholder
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Notes
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Foundations: linear algebra, likelihood and Bayes’ rule
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Classifiers
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Placeholder
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Linear and Quadratic Discriminant Analysis
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Cross-Validation and Bootstrapping
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Regularization, shrinkage and dimension reduction
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Nonlinearity in linear models
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Trees for Regression and Classification
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Support Vector Classifiers
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Unsupervised learning
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Clustering
15.1
K-means
15.2
Heirarchical clustering
15.3
Example: Gene expression in cancer
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Principal components
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Programming Basics
Appendices
Connecting RStudio to your GitHub repository
Instructions for the publishing system: Bookdown
Notes for Statistical Computing & Machine Learning
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Unsupervised learning