Introduction to Matlab Programming with Applications

Location: Room 108, 德田館
Time: 1900 ~ 2200, Wednesday and Saturday

``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

Logic
High school math

Text

No textbook.

References

Matlab

Duane C. Hanselman and Bruce L. Littlefield, Mastering MATLAB, 2013
Craig S. Lent, Learning to Program with MATLAB: Building GUI Tools, 2013
Stormy Attaway, Matlab: A Practical Introduction to Programming and Problem Solving, 3/e, 2013
W. J. Palm III, Introduction to MATLAB for Engineers, 3/e, 2011
Holly Moore, MATLAB for Engineers, 3/e, 2011
R. V. Dukkipati, MATLAB: An Introduction with Applications, 2010
Roger Jang (張智星), MATLAB程式設計《入門篇》, MATLAB程式設計《入門篇》- Exercises

Linear algebra

Stephen H. Friedberg, Arnold J. Insel, and Lawrence E. Spence, Linear Algebra, 4/e, 2002
Gene H. Golub and Charles F. van Van Loan, Matrix Computations, 3/e, 1996
Phil Klein, CS053 Reloaded - The Matrix In Computer Science (Linear Algebra), Brown University, 2014
3Blue1Brown, Essence of linear algebra, YouTube

Numerical methods and analysis

Chih-Jen Lin, Numerical Methods, National Taiwan University, 2016
Cleve Moler, Numerical Computing with MATLAB (online resource)
Matthew J. Zahr, Advanced MATLAB for Scientific Computing, Stanford University
L. Vandenberghe, EE103 - Applied Numerical Computing, UCLA, 2011fa
Aleksandar Donev, Numerical Methods I, Courant Institute of Mathematical Sciences, New York University, 2014fa
Markus Schmuck, Numerical Methods for PDEs, Maxwell Institute for Mathematical Sciences, and the Department of Mathematics, Heriot-Watt University

Data mining and machine learning

Roger Jang, Data Clustering and Pattern Recognition, Department of Computer Science & Information Engineering, National Taiwan University
Piyush Rai, Machine Learning CS771A, Department of Computer Science & Engineering, Indian Institute of Technology Kanpur, 2016fa
Tony Jebara, Machine Learning, Computer Science Department, Columbia University, 2015sp
Tommi Jaakkola, Machine Learning, The Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 2004
Andrew Zisserman, C19 Machine Learning, Information Engineering, Department of Engineering Science, Oxford university, 2015

Artificial Neural Networks and Deep Learning

Roger Jang, Data Clustering and Pattern Recognition, Department of Computer Science & Information Engineering, National Taiwan University
Fei-Fei Li, CS231n: Convolutional Neural Networks for Visual Recognition, Stanford Vision Lab, Computer Science Department, Stanford University, 2017sp
Neural Network Zoo
Artificial neural networks

Optimization

Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Stanford University

Overview

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

Essentials

Variables, data types, and operators
Simple data structures: arrays, cell, structures
Control structures: if-else, for loop, while loop, switch-case, try-catch
Analysis of algorithms and vectorization
Built-in and user-defined functions
2D and 3D plotting
User-controlled input and output
Matrix computation and numerical linear algebra
Graphic user interfaces
Tutorial: Simulink, symbolic programming, MCC/MEX

Applications

Numerical methods (root-finding, curve fitting, finite difference, numerical integration, ODEs, PDEs)
Optimizations
Computational statistics and Monte Carlo simulation
Digital image processing
Time series analysis
Financial engineering
Machine learning and data mining
Neural networks and deep learning

Note that some of these topics are introduced as examples in Essentials.

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 ]

Date	Summary
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;
2017.2.8	[exercises];
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];

Sample Code

matlab/277/code/