Deep Multi-Task & Meta Learning I | Stanford CS330

Explore state-of-the-art multi-task learning and meta-learning algorithms in this graduate-level course, preparing you for research in deep learning.

Introduction

This graduate-level course covers the setting where there are multiple tasks to be solved, and studies how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes self-supervised pre-training for downstream few-shot learning and transfer learning, meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly, and curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer.

Highlights

• Covers state-of-the-art multi-task learning and meta-learning algorithms
• Prepares students to conduct research on these topics
• Focuses on leveraging the structure of multiple tasks to enable more efficient and effective learning

Recommendation

This course is recommended for graduate students and researchers interested in advancing the field of deep learning, particularly in the areas of multi-task learning and meta-learning. The course provides a solid foundation in the latest techniques and equips students with the knowledge and skills to tackle complex learning problems.

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