Mathematical Foundations of Analytics

Catalog description

This course covers the fundamental mathematics needed for further study in data science, machine learning and artificial intelligence. Students will learn the theory and application of linear algebra, analytic geometry, matrix decompositions, vector calculus, probability theory and optimization. Building upon these mathematical foundations, the course culminates with an overview of some key machine learning concepts: linear regression; principal components analysis; density estimation; and support vector machines. The emphasis of this course is on the theory underlying data science methods and machine learning. Requirements/Restrictions: Knowledge of Calc II and Linear Algebra (or equivalent) are required.