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.
Note that some of these topics are introduced as examples in Essentials.
|2017.1.4||program, cpu, memory, memory hierarchy, programming languages (machine code, high-level language), computational solution, algorithm, binary system, data types (integers/floating points), numerical errors, variables, scalars, arrays;|
|2017.1.7||cells, structures, rational operators, logical operators, selection (if-elseif-else, switch-case-otherwise), error and error handling (try-catch), for loops;|
|2017.1.11||while loops, jump statements (break, continue), nested loops;|
|2017.1.14||analysis of algorithm, vectorization, profiling;|
|2017.1.18||functions, call stack, scope of variable, primary/subfunction, anonymous function, recursion;|
|2017.1.21||2D plots, PROJECT: European call option prices (BlackScholesModel.m, EuroCall_MonteCarloSimulation.m, option pricer, Black–Scholes formula);|
|2017.1.25||graphics objects, get/set methods, 3D plots, interpolations, file I/O, matrix computation, system of linear equations, Gauss elimination, 2D Laplace PDE boundary value problem by finite difference, least square error method; feedback sheet;|
|2017.2.11||polynomials, polynomial fitting, overfitting, eigenvalue problems (see The World’s Largest Eigenvalue Problem), singular value decomposition (Image compression by SVD, svd_image_compression.m, Applications of the SVD), gui design, Simulink tutorial, PROJECT: Blurring and edge detection by using FFT (Lecture 10: Discrete Fourier Transform, imageProcessing_LowHighPass.m);|
|2017.2.15||final project demo; feedback sheet; PROJECT: Implied volatility from the market data (TXO.xlsx, Newton's method, ImpliedVolatilityCalculator.m), The SVM classifier, PROJECT: Support Vector Machine (SVM) (LIBSVM), [data];|