Advanced Machine Learning AI

1.5 Credits · Level: Graduate

This course is a continuation of “Machine Learning I” and covers more advanced supervised learning methods as well as unsupervised learning methods. Among the supervised learning methods, the course starts with regression splines, smoothing splines and Generalized Additive Models (GAM). It then covers tree based methods such as CART, Boosting and Random Forests. Among the unsupervised learning methods, the course covers principal component analysis, k-means clustering and hierarchical clustering. The course will emphasize a rigorous treatment of the material as well as their practical business implementation.