DA-GA3001: Modern Topics in Statistical Learning TheoryCenter for Data Science, New York University, Spring 2023 Instructor: Qi Lei Welcome to the DA-GA3001 course webpage for Spring 2023. Course Description:This course is a graduate-level topic course focusing on the theoretical grounding and statistical properties of the modern learning algorithms - with a focus on weakly supervised learning. The intended topics to cover include: basics in machine learning, optimization and generalization bound, followed by the introduction and theoretical understanding surrounding meta-learning, self-supervised learning, and domain adaptation. To benefit from this class, strong linear algebra, probability, and optimization background are required. Students should be familiar with basic machine learning and deep learning concepts. The class consists of 3 units. In the first unit, we will cover the more standard theoretical analysis tools used in deep learning including stochastic gradient, uniform convergence theory and statistical learning theory. After the first unit, the course will move on to specific topics (Unit 2: transfer learning and Unit 3: self-supervised learning). Since some topics covered in this course are quite recent, some content will be based on recent papers instead of a textbook. Resources for the class:Even though the content of this course is not based on a specific textbook, the following materials are good references for certain topics of the course. |