Introduction to Matlab Programming with Applications

Location: Room 108, 德田館
Time: 0930 ~ 1230, from Monday to Friday

``All science is dominated by the idea of approximation.''
-- Bertrand Russell (1872-1970)


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.





Linear algebra

Numerical methods and analysis

Data mining and machine learning with Matlab



Related courses


Additional reading

Wifi Connection


The major topics covered in the short course, if time permitting, are listed below for your reference.



Schedule [ 236, 237, 238, 242, 244, 245, 247, 249, 251, 252, 253, 254, 256, 258, 260, 261, 262, 263, 264, 265, 266, 268, 272, 273 ]

Date Key points and memo
2016.8.22 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;
2016.8.23 selection (if-elseif-else, switch-case-otherwise), error and error handling (try-catch), while loops, for loops;
2016.8.24 nested loops, analysis of algorithm, vectorization, profiling;
2016.8.25 functions, call stack, variable scope, recursion;
2016.8.26 2D/3D plots, interpolations; BlackScholesModel.m, EuroCall_MonteCarloSimulation.m,, Black–Scholes formula;
2016.8.29 matrix computation (system of linear equations, 2D Laplace PDE boundary value problem), finite difference, least square error method;
2016.8.30 polynomial fitting, SVD application: Low-rank approximation for image compression (Applications of SVD: image compression, svd_image_compression.m), file I/O;
2016.8.31 PROJECT: implied volatility from the market data (TXO.xlsx, Newton's method, ImpliedVolatilityCalculator.m), gui design;
2016.9.1 Simulink tutorial, PROJECT: blurring and edge detection by FFT (Lecture 10: Discrete Fourier Transform, imageProcessing_LowHighPass.m), constrainedFminOnPeaks.m;
2016.9.2 final exam (; feedback;