Machine Learning

1.5 credits · Undergraduate

An introduction to machine learning methods including: Supervised learning methods (linear regression models, elastic net, LDA, QDA, KNN, decision tree, random forest, splines, GAM, and multi-task predictions); Unsupervised learning methods in dimension reduction (PCA, t-SNE, UMAP) and in clustering (K-means, clustering, mixture models); and Ensemble learning methods (bagging, boosting, stacking).