COS513: Foundations of Probabilistic Modeling

David Blei (Professor)
419 CS Building
Office hours: Mon/Wed 12:30PM-1:30PM

Indraneel Mukherjee (Assistant)
431 CS Building
Office hours: Tue 2:00PM-4:00PM


Monday/Wednesday 11:00AM-12:20PM, Friend 008

Syllabus and course description

Probabilistic modeling is a mainstay of modern machine learning research, providing essential tools for analyzing the vast amount of data that have become available in science, scholarship, and everyday life. This course will cover the mathematical and algorithmic foundations of this field, as well as methods underlying the current state of the art.

For more details, see the syllabus pdf. The readings are available in a packet at the UStore.

Please sign up for the mailing list. This is the forum for asking questions about the course and discussing the course material.

Scribe notes

Please use LaTex to prepare scribe notes, and please use the template. Below you will find the completed scribe notes.

Lecture 1 V. Krishnamurthy and Y. T. Xi
Lecture 2 H. Zheng and J. Yu
Lecture 3 D. Shue and J. Valentino
Lecture 4 J. Bradic and M. Cucuringu
Lecture 5 D. Eis and V. Kostina
Lecture 6 W. Kim and C. Park
Lecture 7 Y. Luo and M. Simon
Lecture 8 N. Slavov and A. Parikh
Lecture 9 S. Gerrish and C. Wang
Lecture 10 M. Carroll and L. Luo
Lecture 11 H. Goodarzi and S. Jafarpour
Lecture 12 Y. Wu and Y. Chi
Lecture 13 S. Gershman and R. Socher
Lecture 14 J. Fernandes and J. Puente
Lecture 15 A. Lorbert and G. Polatkan
Lecture 16 J. Li and J.C. Niebles
Lecture 17 A. Ungureanu and D. Li
Lecture 20 J. Song and B. Yao
Lecture 21 J. Deng and M. Hoffman
Lecture 22 J. Jiang and M. Wawrzoniak


We require some exposure to probability, such as what is covered in COS341 or COS402, and comfort with computer programming and basic linear algebra. Contact Prof. Blei if you have concerns about your prerequisite coursework.

Course grades and workload

The course grade is based on four items

We emphasize that in the first three items, including the scribe notes, we expect polished and proofread reports. All work should be typeset with LaTex. Writing quality will play a role in the final evaluation.