MSSC 6250: Statistical Machine Learning

The course covers supervised learning and unsupervised learning models and algorithms. Supervised learning methods include various regression and classification methods, and unsupervised learning methods involves dimension reduction and clustering techniques. Topics include Bayesian linear regression, shrinkage and regularization, regression splines, Gaussian processes, logistic regression, discriminant analysis, nearest neighbors, tree-based methods, principal components, K-means, Gaussian mixture clustering, neural networks, etc.