Deep Multi-Task and Meta Learning

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About Course

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.

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Course Content

Deep Multi-Task and Meta Learning

  • Multi-Task and Meta-Learning | Lecture 1 – Introduction & Overview
    00:00
  • Multi-Task and Meta-Learning, | Lecture 2 – Multi-Task & Meta-Learning Basics
    00:00
  • Multi-Task and Meta-Learning, | Lecture 3 – Optimization-Based Meta-Learning
    00:00
  • Lecture 4 – Non-Parametric Meta-Learners
    00:00
  • Lecture 5 – Bayesian Meta-Learning
    00:00
  • Lecture 6 – Reinforcement Learning Primer
    00:00
  • Lecture 7 – Kate Rakelly (UC Berkeley)
    00:00
  • Lecture 8 – Model-Based Reinforcement Learning
    00:00
  • Lecture 9 – Lifelong Learning
    00:00
  • Lecture 10 – Jeff Clune (Uber AI Labs)
    00:00
  • Lecture 11 – Sergey Levine (UC Berkeley)
    00:00
  • Lecture 12 – Frontiers and Open Challenges
    00:00
  • Literature Review 1
    00:00
  • Student Literature Review 2
    00:00

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