Advanced Topics in Machine Learning


Welcome to CS 290C, Advanced Topics in Machine Learning: 
Graphs, Networks, and Causal Modeling


Instructor: Prof Lise Getoor
Course Time and Location: TTH 4:00-5:45 Thimann Lab 101

Office hours: Th 2:00pm-4:00pm and by appointment, E2 341B

The course will study a variety of approaches to causal modeling, with an emphasis on graphical models.   We will survey methods from statistics, economics and computer science.  An emphasis will be places on causal models for network and graph settings.

The class will be highly interactive, collaborative and will require the ability to digest and synthesize material from a variety of disciplines (and hopefully fun!).   The bulk of the work will be a class project (which ideally relates to your research and results in a publication), and reading and presenting papers.    

Suggested background includes machine learning and probabilistic models, however course requirements will not be strictly enforced, email me (, if you need a course enrollment code.   I expect that students will be coming to the course with a variety of backgrounds and will be adjusting the course to accommodate.

Course Format:
This is a seminar course. Each class will consist of presentations and discussion. Students will be required to do a class project for the course (60%) . A significant portion of the grade will be based on class participation, which includes paper presentations, contributions to the forum, and discussion (40%).

Instructors and Assistants

Class Web Page