Skip to main content
Princeton Mobile homeCourses home

Foundations of Probabilistic Modeling

COS 513/SML 513

Info tab content
This course covers fundamental topics in probabilistic modeling, an important area of machine learning research. We learn how to model data and develop algorithms to learn the structure underlying these data for the purpose of prediction and decision-making. We cover several model classes--conditional and unconditional models--and several inference algorithms, including variational inference, the algorithm behind variational auto-encoders. At the end of the course, students should be well-equipped to come up with a probabilistic model and inference algorithm for their data, and use the fitted model for tasks of interest.
Instructors tab content
Sections tab content

Section L01