MATLAB Workshops
Computational Science and Engineering
Foreword
MATLAB is a powerhouse language ubiquitous in engineering applications in academia and industry. This workshop series will introduce you to basic and advanced MATLAB modules and concepts, including a focus on data processing and data analytics workflows.
The EWS Linux machines have everything we need for the workshop. If you plan to use your personal laptop, you’ll need to install a version of MATLAB from MathWorks.
Location
All workshops will be held in the EWS computer laboratory, 1001 Mechanical Engineering Laboratory.
There is no signup for this series—walkins are welcome and encouraged!
Topics
MATLAB Basics
Feb. 22, 1:00 p.m.–3:00 p.m.
We will conduct a handson walkthrough of what MATLAB has to offer as a foundation for later tutorials throughout the semester. We will cover the following topics:

Introduction  MATLAB, programming

Variables(scalar, vector, matrices) and Operators

Functions

Basic numerical examples & matrix solutions

Control flow & matrix definitions
Example: Area of a circle & volume of a sphere (functions)
function [A] = areaOfCircle(r)
A = pi * r^2;
Example: Fahrenheit/Celsius (functions)
function Tf = TempC2F(Tc)
Tf = Tc .* (180/100) + 32;
end
Example: Falling ballistic object (vectorization, functions)
a=9.8; %m/s^2
v=2520; %m/s
x0=0;
t=1;
y=a*t^2+v*t+x0;
t=linspace(0,5,101)
Example: Truss forces (Elementwise & matrix operators)
x = inv(T)*f
x = T \ f;
Example: Control Flow, Define Matrix
% Preallocate a matrix
nrows = 4;
ncols = 4;
myData = ones(nrows, ncols);
% Loop through the matrix
for r = 1:nrows
for c = 1:ncols
if r == c
myData(r,c) = 2;
elseif abs(r  c) == 1
myData(r,c) = 1;
else
myData(r,c) = 0;
end
end
end
MATLAB Numerics
Mar. 1, 1:00 p.m.–3:00 p.m.
 Control Flow in Matlab
 Heat conduction example
 Explicit function vs. Function control
 Radioactive decay chain (systems of linear ODEs) example
 Systems of nonlinear ODEs example
Data Analytics with MATLAB (1)
Mar. 8, 1:00 p.m.–3:00 p.m.
 Data access and data cleaning
Data Analytics with MATLAB (2)
Mar. 15, 1:00 p.m.–3:00 p.m.
 Principle Component Analysis
 Monte Carlo Simulation
Spring Break
Data Analytics with MATLAB (3)
Mar. 29, 1:00 p.m.–3:00 p.m.
 Support Vector Machine
Data Analytics with MATLAB (4)
April 5, 1:00 p.m.–3:00 p.m.
 Classification: Knearest neighbor method, Tree Model
Data Analytics with MATLAB (5)
April 12, 1:00 p.m.–3:00 p.m.
 K means clustering, Hierarchical clustering
Data Analytics with MATLAB (6)
April 19, 1:00 p.m.–3:00 p.m.
 Classification: Linear and Quadratic Discriminant Analysis, Naive Bayes
Data Analytics with MATLAB (7)
April 26, 1:00 p.m.–3:00 p.m.
 Logistic Regression, Regression with Regularization
Data Analytics with MATLAB (8)
May 3, 1:00 p.m.–3:00 p.m.
 Hidden Markov Model
 Data import from sql server