This is the public course website for Machine Learning Theory, Spring 2024.
This course will cover classical machine learning theory (but not necessarily the modern "deep learning theory") and some optimization theory. Topics will include: PAC learning, VC dimension, Rademacher complexity, kernel methods and RKHS, gradient descent, Newton's method, stochastic gradient descent, and continuous-time models of gradient descent.
Ernest K. Ryu, 27-205,
This class will have in-person midterm and final exams.
Good knowledge of the following subjects is required.
Background knowledge in the following areas is helpful but not necessary.