Stanford's CS330 course provides an overview of Multi-Task and Meta-Learning, exploring basics such as optimization-based meta-learning, nonparametric meta-learners, and Bayesian meta-learning. The course includes lectures from experts in the field such as Kate Rakelly (UC Berkeley), Jeff Clune (Uber AI Labs), and Sergey Levine (UC Berkeley). It also covers topics such as lifelong learning and model-based reinforcement learning. The course also includes student literature reviews to evaluate new and existing literature. In this course, students gain a comprehensive understanding of all aspects of multi-task and meta-learning.
Overview
Syllabus
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 1 - Introduction & Overview.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 2 - Multi-Task & Meta-Learning Basics.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 3 - Optimization-Based Meta-Learning.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 4 - Non-Parametric Meta-Learners.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 5 - Bayesian Meta-Learning.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 6 - Reinforcement Learning Primer.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 7 - Kate Rakelly (UC Berkeley).
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 8 - Model-Based Reinforcement Learning.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 9 - Lifelong Learning.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 10 - Jeff Clune (Uber AI Labs).
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 11 - Sergey Levine (UC Berkeley).
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 12 - Frontiers and Open Challenges.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Student Literature Review 1.
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Student Literature Review 2.
Taught by
Stanford Online
