Detailed Course Information |
Fall 2018
Apr 18, 2024 |
|
 |
||
| Select the desired Level or Schedule Type to find available classes for the course. |
| CSCI 2952C - Learning with Limited Labeled Data |
|
As machine learning is deployed more widely, researchers and practitioners keep running into a fundamental problem: how do we get enough labeled data? This seminar course will survey research on learning when only limited labeled data is available. Topics covered include weak supervision, semi-supervised learning, active learning, transfer learning, and few-shot learning. Students will lead discussions on classic and recent research papers, and work in teams on final research projects.
Previous experience in machine learning is required through CSCI 1420 or equivalent research experience. 1.000 Credit hours 1.000 Lecture hours Levels: Graduate, Undergraduate Schedule Types: Primary Meeting Computer Science Department Restrictions: Must be enrolled in one of the following Levels: Graduate |
| Return to Previous | New Search |
|