Learning with Limited Labeled Data
|Offered this year?||Yes|
|When Offered?||Every year|
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 CSCI1420 or equivalent research experience.