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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 sign-up for this series—walk-ins are welcome and encouraged!

Topics

MATLAB Basics

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:

  1. Introduction - MATLAB, programming

  2. Variables(scalar, vector, matrices) and Operators

  3. Functions

  4. Basic numerical examples & matrix solutions

  5. 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: 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