|
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 | |