Math/Theory-Machine Lrning

Catalog description

The purpose of this course is to gain a deeper understanding of the foundational mathematics and theory of machine learning. We will cover a number of mathematical topics that underpin the thinking behind and implementation of machine learning, including topics from linear algebra, probability & statistics, multivariate calculus, information theory, algorithmic analysis, and algorithmic complexity. This includes mathematical frameworks for learning, algorithms for learning concept classes, with an emphasis on proving bounds on the resources (time, space, sample complexity) required by these algorithms, and methods for proving the intractability of certain learning tasks.