Mathematical Foundations of Machine Learning

1 Unit · Graduate

In this course, students are expected to explore some mathematical foundations of modern machine learning under a problem-solving framework. Topics include probability theory, frequentist statistics, Bayesian statistics, tensor algebra, vector calculus, convex and stochastic optimization, stochastic processes and sampling, sequential optimization and dynamic programming. This class strongly emphasizes on developing problem-solving skills.