Topics in Applied Mathematics III: The Mathematics of AI

This course introduces the mathematics knowledge involved in machine learning and artificial intelligence on two levels. In the first half of the quarter, we introduce math needed to understand machine learning practices, i.e. data, models, and algorithms. Topics include advanced notions in linear algebra, probability, statistics, and optimization theories. In the second half of the quarter, we focus on math used to study and analyze machine learning in scientific research. Topics include approximation theory, concentration inequalities, functional analysis, and optimization. This course focuses on the mathematical tools for studying machine learning, rather than implementations of machine learning methods. May be repeated for credit. NOTE: Undergraduates require instructor permission to enroll. Undergraduates interested in taking the course should contact the instructor for permission, providing information about relevant background such as performance in prior coursework, reading, etc.