Advanced MATLAB for Scientific Computing

CME 292 (Advanced MATLAB for Scientific Computing), offered by Institute for Computational & Mathematical Engineering, Stanford University
Updated 19 Jun 2023

CME292 Advanced MATLAB for Scientific Computing

View Advanced MATLAB for Scientific Computing on File Exchange

offered by Stanford ICME ( in collaboration with MathWorks (

Course Description

The goal of this 8-lecture short course is to introduce advanced MATLAB features, syntaxes, and toolboxes not traditionally found in introductory courses; applications will be drawn from various topics from scientific computing. Material will be reinforced with in-class examples and demos involving topics from scientific computing. Students will be practicing the knowledge learned through a mini course project, which will be based on either the suggested topics or a topic of their own choice. MATLAB topics to be covered will be drawn from: advanced graphics and animation, MATLAB tools, data management, code optimization, object-oriented programming, and a variety of toolboxes, including optimization, statistical and machine learning, deep learning, parallel computing, and symbolic math. Students should expect to gain exposure to the tools available in the MATLAB software, knowledge of and experience with advanced MATLAB features, and independence as a MATLAB user. Successful completion of the course requires completion of a mini project.


CME 192 (Introduction to MATLAB) or equivalent programming background in other languages is highly recommended prior to taking this course. Basic knowledge of numerical methods, linear algebra, and machine learning is recommended, but not required.

Course Syllabus

The course syllabus for winter 2022 is available here.

The course syllabus for winter 2023 is available here.


  1. Course Introduction, MATLAB Fundamentals
  2. Graphics and Data Visualization
  3. File Manipulation, Big Data Handling, Integration with Other Languages
  4. Machine Learning with MATLAB
  5. Applied Math with MATLAB
  6. Object Oriented Programming, Efficient Code Writing
  7. Advanced Tools for Images and Signals
  8. Wrap-Up & Additional Topics


The course materials are adapted from a previous version of the course offered by ICME alum Matthew J. Zahr (, and the online resources provided by MathWorks, including the online courses ( and examples ( A more detailed list of sources consulted for the preparation of course materials can be found below.

The materials are reformatted by Xiran Liu (ICME PhD). Special thanks to Dr. Hung Le from ICME and Dr. Reza Fazel-Rezai from MathWorks for guiding the reformation of course materials.

Resources from MathWorks

Cite As

Xiran Liu (2024). Advanced MATLAB for Scientific Computing (, GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2019a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

See release notes for this release on GitHub:


See release notes for this release on GitHub:


To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.