About the course

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

Certificate (PDF)

Topics:

• Ch1 Introduction
• Ch2 Statistical Learning
• Ch3 Linear Regression
• Ch4 Classification
• Ch5 Resampling
• Ch6 Model Selection
• Ch7 Nonlinear
• Ch8 Trees
• Ch9 SVM
• Ch10 Unsupervised