Computational Science and Engineering
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.
All workshops will be held in the EWS computer laboratory, 1001 Mechanical Engineering Laboratory.
There is no sign-up for this series—walk-ins are welcome and encouraged!
Feb. 22, 1:00 p.m.–3:00 p.m.
We will conduct a hands-on 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
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
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
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: K-nearest 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