Fall 2024 Statistics-Computer Science GR6701 section 001

Probabilistic Models and Machine Learnin

Stat ML: Theory to Practi

Call Number 15258
Day & Time
Location
MW 2:40pm-3:55pm
702 Hamilton Hall
Points 3
Grading Mode Standard
Approvals Required None
Instructor Genevera Allen
Type LECTURE
Method of Instruction In-Person
Course Description

Probabilistic Models and Machine Learning is a PhD-level course about how to design and use probability models. We study their mathematical properties, algorithms for computing with them, and applications to real problems. We study both the foundations and modern methods in this field. Our goals are to understand probabilistic modeling, to begin research that makes contributions to this field, and to develop good practices for building and applying probabilistic models.

Web Site Vergil
Department Statistics
Enrollment 29 students (100 max) as of 9:14PM Wednesday, November 20, 2024
Subject Statistics-Computer Science
Number GR6701
Section 001
Division Graduate School of Arts and Sciences
Open To Engineering:Graduate, GSAS
Note PhD students only.
Section key 20243STCS6701G001