Introduction to Matlab Programming with Applications

Location: Room 223B, 德田館
Time: 1330 ~ 1715


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

Goal

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.

Prerequisites

Text

References

Matlab

Linear algebra

Numerical methods and analysis

Data mining and machine learning

Artificial neural networks and deep learning

Optimization

Simulink

Interesting materials

Misc

Additional reading

WiFi

Overview

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

Essentials

Applications

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, 275, 277, 279, 280, 281, 282, 283, 288, 289, 294 ]

Date Summary
2018.1.29 programs, cpu, memory, memory hierarchy, programming languages (machine code, assembly code, high-level language), computational solution, algorithms;
2018.1.30 variables, assignment operator (=), binary system, data types (integers/floating points), numerical errors, scalars, arrays, cells, structures, vectorization, element-by-element operators, rational operators (<, ==, >), logical operators (~, &, |), selections (&&, ||, if-elseif-else);
2018.1.31 selections (switch-case-otherwise), error and error handling (try-catch), for loops, while loops, jump statements (break, continue), nested loops, ;
2018.2.1 analysis of algorithms, performance analysis and speedup, functions, call stack, variable scope, primary/subfunction, anonymous functions;
2018.2.5 recursion, 2D plots, annotation, graphics objects, get/set methods, 3D plots, interpolations, file I/O (exercise), string and regular expressions (raw_data.zip; more exercises can be found: HW09.zip);
2017.2.6 matrix computation, system of linear equations, Gauss elimination, 2D Laplace PDE boundary value problem by finite difference, least square error method (also see least-squares fitting of circles and ellipses), polynomials, polynomial fitting, overfitting issue, eigenvalue problems (see The World's Largest Eigenvalue Problem), singular value decomposition (see image compression by SVD, svd_image_compression.m, SVD applications);
2017.2.7 Simulink tutorial, gui design;
2017.2.8 PROJECT: Blurring and edge detection by using FFT (see Discrete Fourier Transform, imageProcessing_LowHighPass.m); final exam;

Sample Code

Gradebook