COMP 152-04 Machine Learning with Limited Annotation

This course will cover the latest research on machine learning in settings where labeled data is not available. In such settings, classical methods are not necessarily the best approaches for algorithms to learn from data. Students will lead and participate in discussions on recent research publications. Students will also complete a research project applying these new methods. In addition to the specific knowledge about the topic area, students will also gain experience reading and understanding research literature, planning and executing research projects, and communicating about machine learning research. Students are expected to have taken COMP 135 or have equivalent experience, broadly defined.

We will read about ongoing research on how to solve the challenging task of doing machine learning with limited labeled data. We will read a lot of papers and try to understand the problems they solve and their methods. You will do a mini research project during the semester where you either apply an existing method to an application you are interested in or work on developing and evaluating new methods.