EE103/CME103: Introduction to Matrix Methods

This is the website for EE103/CME103, Autumn quarter 2017–18. EE103/CME103 will next be taught in Spring quarter 2017–18 by Professor Brad Osgood.


  • Solutions to the 2017 final exam have been posted.

About EE103

EE103 was taught for the first time during Autumn quarter 2014–15. In the class, we discuss the basics of matrices and vectors, solving linear equations, least-squares methods, and many applications. We'll cover the mathematics, but the focus will be on using matrix methods in applications such as tomography, image processing, data fitting, time series prediction, finance, and many others. Matrix methods should not be a spectator sport. In this course, students use a new language called Julia, developed at MIT, to do computations with matrices and vectors.

EE103 was developed by Stephen Boyd and his band of co-conspirators: Ahmed Bou-Rabee, Keegan Go, Jenny Hong, Karanveer Mohan, Jaehyun Park, and David Zeng.

The class is based on a book that Stephen Boyd and Lieven Vandenberghe (at UCLA) are currently writing. The book is nearly complete; we will post updated versions as they become available.

EE103 is part of the EE and MS&E core requirements, and certified as a Ways of Thinking course for both formal reasoning (FR) and applied quantitative reasoning (AQR). Additionally, this course is approved for the Computer Science BS Math Elective and also satisfies the Mathematics & Statistics requirement in the School of Engineering.