CS229T

How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. This course will cover fundamental concepts and principled algorithms in machine learning. We have a special focus on modern large-scale non-linear models such as deep neural networks. In particular, we will cover topics such as generalization bounds, non-convex optimization, implicit regularization effect, over-parameterization, distribution shift, unsupervised learning, and self-supervised learning.