Prototyping Predictive Analytic Techniques
This introductory session discusses Predictive Analytics basic and Predictive Maintenance applications. The Google online data analysis Colaboratory environment will be explored.
Day 2 class is a continuation of the Predictive Analytics introduction session. Predictive Analytics modeling using linear regression will be investigated. The electrical behavior of the Adafruit Circuit Playground Express light sensor will be explored using a Predictive Analytics model built with Orange 3 software.
This session will discuss the basics of Machine Learning. Applications of Machine Learning (ML) will be provided. The hands-on lab project exercise consists of building a simple ML model for analyzing noise using the Adafruit Circuit Playground Express sound sensor. A sound-activated switch will be implemented using the Adafruit Circuit Playground Express sound sensor.
Day 4 session consists of wiring a potentiometer to a BBC micro:bit and capturing varying voltage levels from the passive electronic component. The potentiometer's output voltage level signal will be analyzed using a linear regression model built using Python code. Also, a dc motor speed controller will be implemented using a potentiometer, the BBC micro:bit, the Adafruit micro:bit cricket and a dc motor.
The final class session will explore the concept of capturing accelerometer data with the BBC micro:bit. The data will be analyzed using an Orange 3 linear regression model. Also, using Python, a taking accelerometer as an assistive technology-based sensor will be explored in this final class session.
Dr. Don Wilcher, an Electrical Engineer, is an Associate Certified Electronics Technician (CETa), a Technical Education Researcher, Instructor, Maker, Emerging Technology Lecturer, Electronics Project writer, and Book Author. His Learn Electronics with Arduino book, published by Apress, has been cited 80 times in academic journals and referenced on patents.
He is the Assistant Professor of Electrical and Computer Engineering at the University of Alabama - Birmingham. His research interest is Embedded Controls, Robotics Education, Machine Learning, and Artificial Intelligence applications and their impact on Personalized Learning, Competency-Based Models curriculum, and instructional development in Mechatronics, Automation, IoT, Electronics, Robotics, and Industrial Maintenance Technologies. He is also the Founder and owner of MaDon Research LLC, an instructional technology consulting, technical training, and electronics project writing company serving Electronics Marketing Media, Technical and Engineering Education companies.