ENGR108: Introduction to Matrix Methods

EE103/CME103 has been renamed ENGR108, effective Autumn quarter 2020–21. ENGR108 satisfies all requirements that EE103/CME103 did, and has the same Ways of Thinking certifications.

It will be taught Winter quarter 2020–21 by Professor Brad Osgood.


  • This quarter we will be using Canvas as our main class webpage. You should refer Canvas for announcements and updates.

  • We'll be using Ed for Q&A and Julia, sign up here: https://edstem.org/us/join/hMtHAW.

  • Sign up on Gradescope using the code D5J68P.

About ENGR108

ENGR108 was originally created as EE103/CME103 by Stephen Boyd and his band of (then undergraduate) co-conspirators: Ahmed Bou-Rabee, Keegan Go, Jenny Hong, Karanveer Mohan, Jaehyun Park, and David Zeng. It was taught for the first time Autumn quarter 2014–15.

ENGR108 covers the basics of vectors and matrices, 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 the language Julia to do computations with vectors and matrices.

The course is suitable for any undergraduate with the prerequisites or equivalent background.

The class is based on a book by Stephen Boyd and Lieven Vandenberghe (at UCLA), which is available on-line.

ENGR108 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.