This is a course on advanced machine learning methods. The students
must have taken a graduate-level machine learning course as prerequisite.
This is an absolute requirement, you will not be able to follow the course
if you have not taken one. This course focuses on developing methodology
and theory with many applications from text mining and Web search
among others as motivating examples. The format of the course consists of lectures
and student presentations. We expect each student to produce interesting research
results, hopefully in the form of a publishable paper. This is a demanding
course, you are advised to take the course only if you can devote substantial
amount of your time.
Topics (subject to change)
- Point processes and event sequence analysis
- Optimal transport and Wasserstein learning
- Particle filters and data assimilation