MATLAB Technical Seminars at University of Iowa 4/17

MathWorks would like invite you to a set of free technical seminars being held at the University of Iowa Thursday April 17th in the Iowa Memorial Union.

Please register if you plan to attend either or both sessions so we can plan accordingly for seating and hand outs:

Both sessions will be held in the Iowa Memorial Union, South Room 179.


Topics and agendas are:


9:45 – 10:00 AM Registration and sign-in. Walk ins are welcome.

Session1, 10:00 AM – Noon

Project Based Learning with MATLAB, Simulink and Low-Cost Embedded Systems


Project-Based learning is extremely effective way to teach and learn advanced engineering concepts, which could otherwise be too abstract.


MATLAB, Simulink, and the new Support Package for low-cost hardware, can help you to go from theory to practice, and easily experiment with concepts in mechatronics, circuit design, programming, controls, robotics, signal, image and video processing.


In this session, we will demonstrate how to design, simulate, optimize and deploy an embedded algorithm from Simulink. Our example involves developing a streetview robot using LEGO MINDSTORMS NXT.


Highlights include:

·         Designing and simulating control algorithm in Simulink

·         Designing advanced event-driven logic using Stateflow

·         Embedding control logic in low-cost hardware like Arduino, LEGO MINDSTORMS NXT and Raspberry Pi without writing any code

·         Monitoring sensor signals and tuning system parameters in real time

·         Analyzing collected sensor data in MATLAB


No prior experience with MATLAB or Simulink is necessary


We will end the session with time for Q&A


1:15 – 1:30 PM Registration and sign-in. Walk ins are welcome.

Session 2, 1:30 – 4:00 PM

Machine Learning with MATLAB


Machine learning techniques are often used for analysis and decision-making tasks such as forecasting, pattern recognition, and data mining. However, implementing and comparing machine learning techniques to choose the best approach can be challenging.


In this free seminar, you will learn about several machine learning techniques available in MATLAB and how to quickly explore your data, evaluate machine learning algorithms, compare the results, and apply the best technique to your problem.


Highlights include unsupervised and supervised learning techniques such as:

·         K-means and other clustering tools

  • Neural Networks
  • Decision trees and ensemble learning
  • Naïve Bayes Classification
  • Linear, logistic and nonlinear regression


We will end the session with time for Q&A