Statistical Learning for Data Science

This course focuses on advanced statistical learning methods and will build on earlier material on model building and machine learning. Topics covered include classification (discriminant analysis, Bayesian inference, density estimation), tree-based and ensemble methods (random forest, boosting, bagging), support vector machines, neural networks, unsupervised learning (principal component analysis, nearest neighbor, k-means clustering, hierarchical clustering).