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Enroll in 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
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.
ENROLLMENT DATES
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. Click here to enroll
