Course Syllabus
Overview of Supervised Learning, Basis Expansions and Regularization, Kernel smoothing, Model assessment and Selection, Model Inference, Additive Models, Trees & Related Methods, Boosting and Additive Trees, Support Vector Machines and Flexibilities, Prototype methods and Nearest Neighbors, Unsupervised Learning, Ensemble Learning, Undirected graphical Models, High dimensional Problems.