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

Prerequisites

Discrete Mathematics, Linear Algebra, Probability and Statistics, Algorithms

Instructor

Lu Wei (Spring 2014, 2015, 2016)

Components

Final Exam, Midterm, Project, Assignments, Paper Presentation