Welcome to the course AI Explorables for Schools!

In this online course, you, as prospective teachers, will playfully explore two major topics of machine learning: artificial neural networks and reinforcement learning. In Modules 1 and 2, you will learn about artificial neural networks, including their structure, training, and mathematical methods. Modules 3 and 4 focus on reinforcement learning, particularly understanding and balancing the strategies used.  

The name speaks for itself:

• It is especially important to us to support playful and exploratory learning. To achieve this, we offer “Explorables” – small, interactive games that run directly in your browser, requiring no additional software.
• We provide suggestions on how you can use these Explorables in your lessons.
• You can test your knowledge with exercises along the way.

This course is primarily designed for teachers and teachers in training in STEM subjects, with a particular emphasis on computer science and mathematics. The content is also suitable for extracurricular activities, such as workshops, study groups, project days, and similar formats. Additionally, interested students (either independently or under the guidance of a teacher), parents, and a general audience can complete the course.

For the German version of this course, click here. 

• Overview of the basic topics of machine learning, in particular, neural networks, gradient descent, exploration vs exploitation, and reinforcement learning
• Introduction to theoretical functions and mathematical concepts
• Teaching content, objectives, and methods for utilizing games and experiments to foster contemporary, exploratory learning experiences

After completing the course I will be able to

• explain the structure and functioning of artificial neural networks.
• describe the gradient descent method and the concept of “exploration vs. exploitation” in machine learning.
• explain the basic principles of reinforcement learning.
• develop my own game strategies based on heuristics (e.g. “experience-based decision rules”).
• compare human learning with artificial learning.
• teach basic concepts, methods, and goals of machine learning with a focus on artificial neural networks and reinforcement learning using the respective AI Explorables creatively and at different levels in class.

None.

IMAGINARY is a think tank dedicated to modern mathematics communication. As a non-profit organization, our goal is to make mathematical knowledge freely accessible and share it worldwide. What sets IMAGINARY apart is its interactive, open-source content, which engages scientists and remains closely connected to current research.

We would like to thank the entire team involved in the implementation of the course. In addition to the listed authors Andreas Matt, Bianca Violet, Eric Londaits, Fabian Graap, Antonia Mey, and Christian Stussak, these are Juliane Sperling, Elisabeth Schaber, Oliver Schön, Karla Schön, Daniel Ramos, and Johanna Marschall.

The Explorables are based on exhibits from the exhibition “I AM A.I. - Artificial Intelligence Explained”, which was developed by IMAGINARY and financed by the Carl Zeiss Foundation.