Mathematics of Machine Learning

1 Course Unit

Machine learning is the study of algorithms (e.g. gradient descent) that learn functions (e.g. deep networks) from experience (e.g. data). Behind this simple statement is a lot of mathematical scaffolding: statistics for handling data, optimization for understanding learning algorithms, and linear algebra to create expressive models. This course provides the background to be able to understand mathematical concepts commonly used in machine learning. Topics include continuous probability, parametric distributions, and concentration inequalities from statistics; inner product spaces, functional analysis and Hilbert spaces from linear algebra; and multivariate calculus, Taylor’s theorem, and convexity from optimization. Fall or Spring Also Offered As: ENM 3333 Prerequisites: Students must take CIS 1600 and STAT 4300 and (MATH 2400 or ESE 2030) before taking CIS 3333. 1 Course Unit