Overview

This is a new undergraduate course, presenting a unified introduction to linear algebra and optimization.

We will not assume any prior knowledge of linear algebra and will start from the basics including vectors, matrices, eigenvalues, singular values and least squares. We will somewhat downplay solving examples by hand and will instead emphasize conceptual, geometric and computational aspects.

Building on insights from linear algebra, we will cover the basics of optimization including convex optimization, linear/quadratic programming, gradient descent and regularization. We will explore a variety of applications in science and engineering where the tools we have developed give powerful ways to learn from data.