Domain adaptation is a field in machine learning and transfer learning that focuses on learning from one data distribution to perform well on a different but related data distribution. It is particularly useful in scenarios like spam filtering, where a model needs to be adapted from one user's emails to another user who receives different types of emails. When there are multiple source distributions available, it is referred to as multi-source domain adaptation.
Stanford University
Fall 2022
This course emphasizes leveraging shared structures in multiple tasks to enhance learning efficiency in deep learning. It provides a thorough understanding of multi-task and meta-learning algorithms with a focus on topics like self-supervised pre-training, few-shot learning, and lifelong learning. Prerequisites include an introductory machine learning course. The course is designed for graduate-level students.
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