Statistical Machine Learning

Catalog description

Basics of statistical learning. Data visualization. Linear, nonlinear and logistic regression, variable selection and regularization. Linear classification methods. Smoothing methods. Tree-based methods. Support vector machines. Unsupervised learning: principal components analysis and clustering. The R and/or Python software will be used