Lecture, three hours. Requisites: courses 115A, 131A, or equivalent. Introduction to mathematical and theoretical aspects of machine learning and artificial intelligence. Topics include probability and high-dimensional statistics, neural networks, deep learning, artificial intelligence, modern architectures, approximation theory, optimization, and error estimates. S/U or letter grading.