Skip to main content
Help

Advanced MATLAB for Scientific Computing

About This Course

The course is aimed for participants working or conducting research in scientific computing. Covered topics in scientific computing will include numerical linear algebra, numerical optimization, ODEs, and PDEs. Relevant applications areas include machine learning, electrical engineering, mechanical engineering, and aeroastro.

There will be seven interactive based lectures with application based assignments to follow.

Participants will be introduced to advanced MATLAB features, syntaxes, and toolboxes not traditionally found in introductory courses. Material will be reinforced with in-lecture examples, demos, and homework assignment involving topics from scientific computing.

MATLAB topics will be drawn from: advanced graphics (2D/3D plotting, graphics handles, publication quality graphics, animation), MATLAB tools (debugger, profiler), code optimization (vectorization, memory management), object-oriented programming, compiled MATLAB (MEX files and MATLAB coder), interfacing with external programs, toolboxes (optimization, parallel computing, symbolic math, PDEs).

Thanks to the support from MathWorks, a free MATLAB license is provided for participants taking the course.

Prerequisites

There are no requirements for the course. Basic knowledge of MATLAB through an introductory course or work experience is highly recommonded. Knowledge of linear algebra and optimization is also recommended.

Course Instructor

Course Staff Image #1

Danielle Maddix

Danielle Maddix is a fifth year PhD Candidate in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. She uses MATLAB in her research in developing stable and accurate methods for computational fluid dynamics. She also focuses on devising computationally efficient and parallel algorithms. She has been the instructor for the Advanced MATLAB for Scientific Computing on-campus course at Stanford for the past year.

FAQ

The course already started--can I still join?

You can enroll in this course all the way up to the course end date of December 15, 2017. All assignments are due December 11th. If you are enrolling late, please note that the course is designed to be an eight-week experience with approximately eight hours per week of coursework.

How much time or effort might one expect to spend on the course each week?

Approximately eight hours. The lecture videos per week are about 1.5 hours. Each short video has optional exercises to practice the material learned in lecture. There is a required and graded Assessment section at the end of each lecture.

Are the exercises under each video graded?

No, they are to practice the material from lecture and help prepare you for the graded assignment.

Will a Statement of Accomplishment be offered to participants in the public course?

Yes, a Statement of Accomplishment will be provided to participants with a cumulative score of 70% or higher on the six assessments.

Will a MATLAB license be included with the course?

Yes, thanks to MathWorks, a MATLAB license is provided for the dates that the course will run.

Should one use MATLAB during the lectures?

It is not required, but it helps with the learning experience to follow along with the in-class demos. It is highly recommended to stop the videos and try the in-class exercises on your own and then resume the video to see the solution.

Are there starter code files for the Assessments?

Yes, at the beginning of each Assessment section, there is the problem description in .m, .mlx and .pdf formats and then the .m starter code files.

Am I allowed more than one attempt for questions in the course?

Yes, you may input as many answers to the same question as you like, as long as it is before the Assessment due date. After the due date, the solutions will be posted.

  1. Course Number

    AdvMATLAB
  2. Classes Start

    Oct 10, 2017
  3. Classes End

    Dec 15, 2017
  4. Estimated Effort

    8 hours per week
  5. Price

    Free

Our Research Community

Stanford University pursues the science of learning. Online learners are important participants in that pursuit. The information we gather from your engagement with our instructional offerings makes it possible for faculty, researchers, designers and engineers to continuously improve their work and, in that process, build learning science.

By registering as an online learner, you are also participating in research...

Read Terms of Service and Privacy Policy.

Image credit: Jarekt(Own Work) [Public domain], via Wikimedia Commons
Enroll