EE103/CME103: Introduction to Matrix Methods

Welcome to EE103/CME103, Autumn quarter 2016–17.


  • This is the website for EE103/CME103, Fall Quarter 2016–17.

  • The finals are graded and ready for pickup from the homework bin. The solutions are posted on the final exam page.

  • Final grades have been posted to the registrar.

About EE103

EE103 is a new course that was taught for the first time Autumn quarter 2014–15. It covers 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. Eventually, the course will be suitable for any undergraduate; but for the first few offerings, while we work out the bugs, we are targeting students in EE, CS, and MS&E. But anyone up for it is welcome.

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.

EE103 is based on a book that Stephen Boyd and Lieven Vandenberghe (at UCLA) are currently writing. The book is only in draft form now; we will post updated versions as they become available.

Matrix methods should not be a spectator sport. Students will use a new language called Julia, developed at MIT, to do computations with matrices and vectors.

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). It is an approved course for the Computer Science BS Math Elective, and the Mathematics & Statistics requirement in the School of Engineering.