Deep Multi-task and Meta Learning
Fee may apply
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, fill out this course application, and be sure to complete your NDO application.
Prospective students who complete the course application will be notified of their application status by September 6th. 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
- Chelsea Finn
- 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 23 - December 6, 2019|
Provides Stanford University credit that may later be applied towards a graduate degree or certificate. Includes access to online course materials and videos for the duration of the academic quarter. Starting Autumn 2016 there is a $100 fee per course for courses dropped before the drop deadline. Click here for more information about our Registration Policies.
NotesEnrollment Dates: August 1 to September 9, 2019
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.
This course may not currently be available to learners in some states and territories.