OPTIMIZATION FOR DATA SCIENCE

Modern statistics and machine learning rely heavily on optimization. Estimation, inference, and prediction often reduce to solving structured optimization problems such as least squares, maximum likelihood estimation, regularized regression, empirical risk minimization, and variational inference. This course introduces the optimization principles underlying modern statistical and machine learning methods. Students will study convex optimization, gradient-based algorithms, stochastic optimization, and large-scale learning problems, with applications to regression, classification, deep learning, and probabilistic modeling.