EECE 501

Mathematical Methods for Electrical and Computer Engineering

The course is a refresher of the mathematical foundations required for understanding and devising methods used in electrical and computer engineering disciplines and machine learning models. Through four central applications in machine learning — regression, dimensionality reduction, density estimation, and classification — the course walks students through how practical problems are formulated into mathematical models and how mathematical tools are applied to solve these problems. Math tools covered in the course include linear algebra, vector calculus, probability, matrix decompositions, and continuous optimization. The course also serves as a service course for other graduate courses in which students are assumed to have a firm grasp of those foundations. The target audiences are mainly Master’s students, including those who are returning to school after working in industry for example.

3 credits