Course Description

Artificial intelligence (AI) and machine learning (ML) can seem like incredibly challenging topics to get into. Most solutions involve complex software and a cloud-based platform that performs the learning. This course provides you with a simple and understandable approach to using elementary ML. It only requires your PC and a source-code neural network that works on Arduino and other microcontrollers

In this course, you’ll learn about the basic building block of ML, neural networks. After covering some of the history of its development and what today’s AI is capable of, you’ll see how artificial neurons learn basic capabilities through software examples. With simple learned functionality, like replicating an AND gate, behind us, we’ll learn why learning an XOR gate’s functionality was challenging for early artificial neuron approaches and how that challenge was overcome.

With this light touch on the theory complete, we’ll explore how this simple little neuron can be used as part of an autonomous driving system to detect the color of traffic lights. Until this point, all the code demonstrated runs on a PC using Processing.

The final section shows how the same (lightly modified) code runs on an Arduino to implement the same traffic light color detecting application. Due to their limited performance, microcontrollers take a long time to learn new capabilities. To speed up learning, an approach for creating the “learned capability” is provided by coupling the performance of your PC with the Arduino.

After that, the world is your oyster! You’ll have a neural network suited to execution on a microcontroller and a tool to teach it new things for your own projects – who knows what you’ll make..

Prerequisite knowledge

Before attending this class, we expect that you should: