Stanford CS330: Deep Multi-Task and Meta Learning

Dive into an 18-hour Stanford University course exploring the cutting-edge fields of multi-task and meta-learning in artificial intelligence. Begin with an introduction and overview, then progress through fundamental concepts, optimization-based techniques, non-parametric meta-learners, and Bayesian approaches. Gain insights into reinforcement learning, including model-based methods, and explore lifelong learning strategies. Benefit from guest lectures by renowned experts from UC Berkeley and Uber AI Labs, offering diverse perspectives on the subject. Conclude by examining frontiers and open challenges in the field, and engage with student literature reviews to deepen your understanding of current research trends.

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.