Instructor: Doug Downey
Email: ddowney <at> eecs <dot> northwestern <dot> edu
Office Hours: Monday 4:00PM-5:00PM, Ford 3-345
Teaching Assistant (part-time): Nishant Subramani
Email: nishant.subramani23 <at> gmail <dot> com
Office Hours: Monday 4:00PM-5:00PM, Ford 3-345 (joint with instructor office hours)
Required Textbook: Koller and Friedman. Probabilistic Graphical Models: Principles and Techniques. The MIT Press.
Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how inference and learning are performed in the models, and how the models are utilized for machine learning in practice.
The course objective is for students to gain an understanding of how graphical models can be used to represent probability distributions, how to perform inference and learning in the models, and how the models are applied to natural language. Topics include directed and undirected graphical models, exact and approximate inference methods, and supervised and unsupervised parameter and structure learning.