Course Description

This graduate level machine learning course develops a foundation for research on intelligent data processing. The topics that we plan to cover in this course include the following: classification, regression, clustering, sequence modeling, recommender problems, generative and discriminative models, model selection and generalization issues, transfer learning, scalability issues, knowledge representations, and various applications.

12 Credits


Discrete Mathematics, Linear Algebra, Probability and Statistics, Algorithms


Lu Wei (Spring 2014, 2015, 2016)


Final Exam, Midterm, Project, Assignments, Paper Presentation