Deep Multi-Task and Meta Learning

  • Course level: Beginner


Deep Multi-Task and Meta-Learning

While deep learning has had great success in supervised and reinforcement learning issues like image classification, speech recognition, and game-playing, these models are specialized to a large extent for the specific task they are trained for. This course will examine how the structure resulting from numerous tasks can be used to learn more efficiently or effectively in a situation where there are multiple tasks to solve. This includes the following:

goal-conditioned reinforcement learning approaches that take advantage of the structure of the specified goal space to learn a large number of tasks much more quickly

meta-learning approaches aimed towards learning efficient learning algorithms capable of fast-learning new tasks

Where a problem necessitates learning a series of activities, curriculum, and lifelong learning, utilizing their shared structure to enable knowledge trajectories

This Deep Multi-Task Course is For

This is a post-graduate course. Students will be able to comprehend and use state-of-the-art multi-task learning and meta-learning algorithms by the end of the course and will be prepared to pursue research on these issues.

Topics for this course

14 Lessons

Deep Multi-Task and Meta Learning

Multi-Task and Meta-Learning | Lecture 1 – Introduction & Overview00:00:00
Multi-Task and Meta-Learning, | Lecture 2 – Multi-Task & Meta-Learning Basics00:00:00
Multi-Task and Meta-Learning, | Lecture 3 – Optimization-Based Meta-Learning00:00:00
Lecture 4 – Non-Parametric Meta-Learners00:00:00
Lecture 5 – Bayesian Meta-Learning00:00:00
Lecture 6 – Reinforcement Learning Primer00:00:00
Lecture 7 – Kate Rakelly (UC Berkeley)00:00:00
Lecture 8 – Model-Based Reinforcement Learning00:00:00
Lecture 9 – Lifelong Learning00:00:00
Lecture 10 – Jeff Clune (Uber AI Labs)00:00:00
Lecture 11 – Sergey Levine (UC Berkeley)00:00:00
Lecture 12 – Frontiers and Open Challenges00:00:00
Literature Review 100:00:00
Student Literature Review 200:00:00

Enrolment validity: Lifetime


  • Graduate Level