COS 513: Foundations of Probabilistic Modeling
This course covers fundamental topics in probabilistic modeling and allows you to contribute to this important area of machine learning and apply it to your work. We learn how to model data arising from different fields and devise algorithms to learn the structure underlying these data for the purpose of prediction and decision making. We cover several model classes--including deep generative models--and several inference algorithms, including variational inference and Hamiltonian Monte Carlo. Finally, we cover evaluation methods for probabilistic modeling as well as tools to challenge our models' assumptions.
Semester:
Fall17
Lectures:
Monday,Wednesday, 3:00-4:20
Location:
Friend Center 004
Faculty
Barbara Engelhardt
Office:
Extension:
Email:
bee
Additional Information
The Graduate Coordinator is Nicki Mahler
Email:
ngotsis
Office:
Computer Science 213
Extension:
5387