CS 330: Deep Multi-Task and Meta Learning Fall 2020, Class: Mon, Wed 1:00-2:20pm

Description:

While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes:

goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster

meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly

curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer

This is a graduate-level course. By the end of the course, students will be able to understand and implement the state-of-the-art multi-task learning and meta-learning algorithms and be ready to conduct research on these topics.

Format:

The course will include live lectures over zoom, three homework assignments, a fourth optional homework assignment, and a course project. The lectures will discuss the fundamentals of topics required for understanding and designing multi-task and meta-learning algorithms in both supervised learning and reinforcement learning domains. The assignments will focus on coding problems that emphasize these fundamentals. Finally, students will present a short spotlight of their project proposal and, at the end of the quarter, their completed projects.



Prerequisites:

CS 229 or an equivalent introductory machine learning course is required. CS 221 or an equivalent introductory artificial intelligence course is recommended but not required.

Lecture Videos:

If you are looking for publicly-available lecture videos from the Fall 2019 offering, they are here. Other materials from the Fall 2019 offering are here. Lecture videos from this Fall 2020 offering will be processed and made publicly available after the course. For students enrolled in the course, recorded lecture videos will be posted to canvas after each lecture.