Directory of Classes

NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information.


Fall 2022 Statistics-Computer Science GR6701 section 001
FOUNDATIONS OF GRAPHICL MODELS
FOUNDATIONS OF GRAPHICL M

Call Number 13872
Day & Time
Location
TR 8:40am-9:55am
312 Mathematics Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor David Blei
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 104 students (116 max) as of 5:21PM Thursday, September 29, 2022
Subject Statistics-Computer Science
Number GR6701
Section 001
Division Graduate School of Arts and Sciences
Open To Engineering:Graduate, GSAS
Campus Morningside
Note PhD students only.
Section key 20223STCS6701G001

Home      About This Directory      Online Bulletins      ColumbiaWeb      SSOL
SIS update 09/29/22 17:21    web update 09/29/22 20:37