Advanced MATLAB for Scientific Computing

version 1.1 (8.26 MB) by Xiran Liu
CME 292 (Advanced MATLAB for Scientific Computing), offered by Institute for Computational & Mathematical Engineering, Stanford University

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Updated 10 Mar 2022

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CME292 Advanced MATLAB for Scientific Computing

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Winter Quarter 2022, offered by Stanford ICME in collaboration with MathWorks

Schedule: Winter 2022, Jan 3rd-Jan 31th, Mon/Wed 5:30-7:00pm

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.

Prerequisites

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.

Topics

Topic Lecture Notes
MATLAB Fundamentals Lecture 1
Graphics and Data Visualization Lecture 2 Part 1
Efficient Code Writing Lecture 2 Part 2
System and File Manipulation Lecture 3 Part 1
Big Data Handling Lecture 3 Part 2
Applied Math - Numerical Linear Algebra Lecture 4 Part 1
Applied Math - Numerical Optimization Lecture 4 Part 2
Applied Math - Symbolic Toolbox, ODE, and PDE Lecture 4 Part 2
Statistical and Machine Learning Lecture 5 Part 1
Deep Learning Lecture 5 Part 2
Object Oriented Programming Lecture 6 Part 1
Using MATLAB with Other Programming Languages Lecture 6 Part 2
Image Processing, Computer Vision, and Image Acquisition Lecture 7 Part 1
Signal Processing, Audio, and DSP System Lecture 7 Part 1
Additional Topics

Acknolwedgement

The course materials are adapted from a previous version of the course offered by ICME alum Matthew J. Zahr (https://mjzahr.github.io/teach-stanford-cme292-spr15.html), and the online resources provided by MathWorks, including the online courses (https://matlabacademy.mathworks.com/) and examples (https://www.mathworks.com/help/examples.html). 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 (2022). Advanced MATLAB for Scientific Computing (https://github.com/xr-cc/CME292_WI22/releases/tag/1.1), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2019a
Compatible with any release
Platform Compatibility
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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.