Credits 3. 3 Lecture Hours.
Theory and practice of deep learning, including topics concerning approximation, generalization and optimization; study of the theory of universal approximation, stochastic gradient-based optimizers and statistical learning bounds, but also computational aspects including backpropagation and batch normalization.
Prerequisites: MATH 304, MATH 251, MATH 411, and MATH 679 or equivalent; approval of instructor.