This short course is designed for the students who want to learn Matlab programming without any experiences before. The students will be introduced to Matlab features and syntaxes. Besides, the fundamentals of programming concepts are delivered with elegant algorithms. You are expected to be capable to implement programs with Matlab independently after this class. Moreover, I expect that you could feel more confident of learning more programming languages 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.
Simulink, symbolic programming, and MCC/MEX are introduced by simple examples.
Note that some of these topcis are introduced as examples in Essentials.
| Date | Key points and memo |
|---|---|
| 2016.10.19 | program, cpu, memory, memory hierarchy, programming languages (machine code, high-levele language), computational solution, algorithm, binary system, data types(integers/floating points), numerical errors, variables, scalars, arrays, cells, structures, rational operators, logical operators; |
| 2016.10.22 | selection (if-elseif-else, switch-case-otherwise), error and error handling (try-catch); |
| 2016.10.26 | (no class); |
| 2016.10.29 | (no class); |
| 2016.11.2 | while loops, for loops, nested loops, analysis of algorithm; |
| 2016.11.5 | vectorization, profiling, functions, call stack, scope of variable; |
| 2016.11.9 | primary/subfunction, anonymous funtion, recursion, 2D plots, PROJECT: European call option prices (BlackScholesModel.m, EuroCall_MonteCarloSimulation.m, http://www.math.columbia.edu/~smirnov/options13.html, Black–Scholes formula); |
| 2016.11.12 | graphics objects, get/set methods, 3D plots, interpolations, matrix computation, system of linear equations; |
| 2016.11.16 | Gauss elimination, 2D Laplace PDE boundary value problem by finite difference, least square error method, polynomials, PROJECT: Internal rate of return (IRR), polynomial fitting, overfitting, eigenvalue problems (see The World’s Largest Eigenvalue Problem); |
| 2016.11.19 | singular value decomposition, PROJECT: Low-rank approximation for image compression (Image compression by SVD, svd_image_compression.m, Applications of the SVD), file I/O, PROJECT: Momentum strategy in Taiwan Stock Market (tai2000_2016_all.xls); |
| 2016.11.23 (@ Room 106) | gui design, Simulink tutorial, PROJECT: Blurring and edge detection by using FFT (Lecture 10: Discrete Fourier Transform, imageProcessing_LowHighPass.m); |
| 2016.11.26 (@ Room 106) | final project demo, Artificial neural networks, Neural networks, Neural Network Zoo, Convolutional Neural Networks (CNNs), PROJECT: Support Vector Machine (SVM) (LIBSVM, The SVM classifier), PROJECT: Implied volatility from the market data (TXO.xlsx, Newton's method, ImpliedVolatilityCalculator.m); feedback; |
TBA