This short course is designed for the students, who want to learn Matlab programming without any experiences before. We will demonstrate Matlab features and deliver the essence of programming concepts with elegant algorithms. The students are expected to implement programs with Matlab independently after 30-hour lecture. If capable, you could feel more confident of learning other programming languages (by youself, of course) and dealing with advanced topics in the future.
The major topics covered in the short course, if time permitting, are listed below for your reference.
You could see the complete list of toolboxes offered by Matlab. If you want to own a license of Matlab, please check how to buy for student use.
| Date | Summary |
|---|---|
| 2018.8.29 | programs, cpu, memory, memory hierarchy, programming languages (machine code, assembly code, high-level language), computational solution, algorithms, binary system, data types (integers/floats), numerical errors; |
| 2018.9.1 | assignment operator (=), scalars, arrays, cells, structures, vectorization, element-by-element operators, rational operators (<, ==, >), logical operators (~, &, |), selections (&&, ||, if-elseif-else, switch-case-otherwise), error and error handling (try-catch); |
| 2018.9.5 | for-each loops, while loops, jump statements (break, continue), nested loops; |
| 2018.9.8 | analysis of algorithms, profiling and speedup, functions, call stack, variable scope; |
| 2018.9.12 | primary/subfunction, anonymous functions, recursion, 2D plots; |
| 2018.9.15 | 3D plots; |
| 2018.9.19 | (FYR) live editor (example), gui design, file I/O, string and regular expressions; |
| 2018.9.22 | (no class) |
| 2017.9.26 | matrix computation, system of linear equations, Gauss elimination, 2D Laplace PDE boundary value problem by finite difference (fdm_example.m), least square error method, polynomials; |
| 2018.9.29 | polynomial fitting, overfitting issue, eigenvalue problems (see The World's Largest Eigenvalue Problem), singular value decomposition (see image compression by SVD, svd_example.m, SVD applications); |
| 2018.10.3 | final project demonstration; Simulink tutorial, symbolic programming, parallel computing, optimization tutorial, PROJECT: Blurring and edge detection by using FFT (see Discrete Fourier Transform, illustration for Fourier transform, imageProcessing_LowHighPass.m), k-means clustering, gpu acceleration; |