6.S084/18.S096 Linear Algebra and Optimization

Fall 2020

This is the pilot offering of a new course in 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 in 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.

Relation to 18.06: This course will count towards the linear algebra requirement for Math majors and for EECS MEng students! Also, you should not take this course if you have already taken 18.06. Both Math and EECS will not give you credit for the course if you've taken 18.06 already.

Announcement: There will be no recitation the first week of classes. The first recitation will be on Tuesday, September 8th.

Course Information


Here is a tentative syllabus for the course. We will add links to instructor notes as we go along.

Instructor Notes


Here are links to other courses with overlapping content: