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 Autumn quarter 2020–21 by Professor Stephen Boyd.

Announcements

  • Week 8's schedule has been modified. Lectures for the entire week will be released on Sunday, and sections will be optional with a recording available.

  • The review guide has been updated for quiz 2.

  • The second quiz has been released and is due 10/29 at 10am PST. Check Ed for details and email course staff or post privately on Ed with any questions.

  • Homework 6 has been posted.

  • Professor Boyd’s office hours are on Thursdays from 9-10am PST.

  • Additional office hours are on Thursday from 12-1pm PST and 4-5pm PST.

  • We'll be using Ed for Q&A and Julia, sign up here: https://us.edstem.org/join/E6wcMs.

  • Sign up on Gradescope using the code MEZNNN.

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.