Course Description
Rigorous foundations for understanding convergence, continuity, and approximation in machine learning. Metric spaces, topology, and compactness. Sequences and series: convergence criteria, dominated convergence. Differentiation and integration in multiple dimensions. Measure theory introduction: sigma-algebras, Lebesgue measure, measurable functions. Probability as measure theory. Applications: convergence of gradient descent, universal approximation theorem proofs.