MAT-408: Mathematical Foundations of Machine Learning

Investigates the mathematical foundations for machine learning models including vector and affine spaces, inner products, orthogonal projections, matrix decompositions, higher-order gradients, multivariate Taylor series, discrete and continuous probability distributions, Bayes' Theorem, and optimization of continuous functions. These topics serve as foundation to some basic pillars of machine learning algorithms including regression, dimensionality reduction, density estimation, and formal classification. Prerequisite: MAT-213: Calculus III and MAT-243: Linear Algebra. [ 3 credits ]