Deep Multi-task and Meta Learning
Deep learning has achieved remarkable success in supervised and reinforcement learning problems including image classification, speech recognition, and game playing. These models are, however, 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. You will explore goal-conditioned reinforcement learning techniques that can increase learning speed of multiple tasks. You will discover how meta-learning methods can be used to learn new tasks quickly. You will learn how leverage the shared structure of a sequence of tasks to enable knowledge transfer. Through this course, you will develop and advance highly-sought after skills in the field of AI.
Please note: the course capacity is limited. To be considered for enrollment, join the wait list and be sure to complete your NDO application. By September 1, an additional course application will be sent out to those who join the waitlist.
Prospective students who complete the course application will be notified of their application status after September 1. Only applicants with completed NDO applications will be admitted should a seat become available.
What you will learn
- How to understand and implement state-of-the-art multi-task learning
- How to execute meta-learning algorithms
- How to leverage the structure arising from multiple tasks to learn more efficiently or effectively
- How to conduct research in these areas effectively
- Multi-Task Supervised Learning
- Bayesian Models and Deep Probabilistic Meta-Learning Approaches
- Model-Based Reinforcement Learning for Multi-Task Learning
- Learning Optimizers, Learning Rules, and Architectures
Note on Course Availability
This course is typically offered Autumn quarter.
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.
|Dates:||September 14 - November 20, 2020|
Computer Science Department Requirement
Students taking graduate courses in Computer Science must enroll for the maximum number of units and maintain a B or better in each course in order to continue taking courses under the Non Degree Option.
Pre-registration for this course will secure your enrollment request and ensure timely processing of your application for potential course approval. Please note: course enrollment will be confirmed after September 1, 2020; after completing your pre-registration, no further action is required on your part.
This course may not currently be available to learners in some states and territories.