Course Meeting Times

Lectures: 3 sessions / week, 1 hour / session


Course Description

This course provides the fundamental computational toolbox for solving science and engineering problems. Topics include review of linear algebra, applications to networks, structures, estimation, finite difference and finite element solutions of differential equations, Laplace's equation and potential flow, boundary-value problems, Fourier series, the discrete Fourier transform, and convolution. We will also explore many topics in AI and machine learning throughout the course.


Strang, Gilbert. Computational Science and Engineering. Wellesley, MA: Wellesley-Cambridge Press, 2007. ISBN: 9780961408817.

Problem Sets

The very important part of the course will be problem solving in every week's homework. The homework problem sets will consist of both theoretical and numerical questions. No late copy will be allowed, but the lowest score will be dropped. Please use MATLAB notation to describe algorithms. Use of MATLAB/Python/Julia for tedious calculations is encouraged; however, you need to know how to do the basic algorithms taught in the course by hand, at least for small matrices.


This course has a midterm exam and a take-home final exam.


Grading is a means rather than an end. It serves as a point of reference for your current study progress rather than a penalty for what you did or didn’t do, or a representation of who you are. I am proposing two grading schemes, and your final grade will be the better of the two:

  • Average of all homework except for the two weakest ones (50%), midterm (25%), and final (25%)
  • Average of all homework except for the weakest one (70%), midterm (15%), and final (15%)