CS 598 LTL - Learning to learn
Last offered Fall 2022
Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.
There has been a recent resurgence of interest in learning to learn, or meta-learning. In the standard machine learning paradigm, a model is trained on a set of examples and is specialized for the single task it is trained for. By contrast, meta-learning is performed on a set of tasks and leverages prior experiences when tackling a new task. This course will cover foundation principles, historical perspective, and recent progress of meta-learning. We will position meta-learning with respect to related areas, such as transfer learning, multi-task learning, and continual learning. The course will also discuss various applications of meta-learning in the fields of computer vision, natural language processing, reinforcement learning, and robotics. Students will be required to present and critique research papers and perform a related research project. By the end of the course, students will be able to understand and implement the state-of-the-art meta-learning algorithms and be ready to co
|Learning to learn||LTL||40107||S14||4||1530 - 1645||T R||4025 Campus Instructional Facility||Yuxiong Wang|