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
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.
|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.
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.