CS 598 HAZ

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Transfer Learning While modern deep learning has achieved remarkable success in supervised learning, including image classification, speech recognition, machine translation, and game playing, this success crucially hinges on the assumption that the training data distribution (approximately) matches the test data distribution. This course will cover topics related to machine learning under the scenario where the training and test distributions are related, but not the same. We will study how to quantify the relatedness between distributions, tasks, and how the structure between them could be used to facilitate more efficient and effective learning. In particular, this course will cover the following topics, often from a representation learning perspective: domain adaptation/generalization, multitask learning, meta-learning, and adversarial robustness. Suggested Prerequisites: CS 225, and CS 446, Machine Learning or an equivalent introductory course on machine learning. For up-to-da