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KI-Explorables für die Schule 2
AI Campus Original
AI Explorables for Schools

Want to go on a treasure hunt with AI? In this course, you will playfully explore different topics of machine learning, such as artificial neural networks and reinforcement learning, and receive appropriate didactic materials for the classroom. No previous knowledge is necessary.

Start date
anytime
Duration
5 modules à 120 minutes
Language
English

Overview

Welcome to the course AI Explorables for Schools!


This course is mainly aimed at teachers and aspiring teachers of STEM subjects with a focus on teaching computer science and mathematics. You may also our content outside of normal school lessons, for example in workshops, study groups, project days, and much more. 

Nomen est omen:

  • We particularly support playful and explorative learning. Therefore, each module starts with a small interactive experiment - without much ado or explanation, you can try things out first.
  • For this purpose, we offer "Explorables" - short, interactive online games that run directly in your browser and do not require any additional software.
  • We offer suggestions on how to use these Explorables in class.

You'll explore two major topics in machine learning: Modules 1 and 2 focus on artificial neural networks, their structure, training, and mathematical methods (simplified). Modules 3 and 4 focus on reinforcement learning, especially on learning and balancing different strategies. For each of these two focal points, there is a module that conveys the theoretical content (in a playful way!) and a second module that focuses on the playful aspect.

Which topics will be covered?

  • Neural Networks
  • Gradient Descent
  • Exploration vs. Exploitation
  • Reinforcement Learning
     

What will I achieve?

After completing the course I will be able to
 

  • identify and communicate basic topics in machine learning
  • describe the structure of artificial neural networks and the flow of data given an input
  • explain basic concepts such as training, training data, and accuracy
  • describe the influence of the size and representativeness of the training data on the quality of the output
  • interpret the visually displayed gradient for one- and two-dimensional cases
  • recreate and train a physical AI
  • develop game strategies based on own heuristics
  • explain the concept of rewards and punishments for events in the context of reinforcement learning
  • describe and justify the effectiveness of different strategies in terms of expected outcomes
  • teach the topics mentioned above on different levels and engage and inspire my pupils on the topic of AI.
     

Which prerequisites do I need to fulfill?

  • No requirements

Who is offering this course?

IMAGINARY is a think tank for modern mathematics communication. Our goal as a non-profit organization is to make mathematical knowledge freely available and to spread it to all corners of the earth. Special features of IMAGINARY are the interactive and open-source content, which involves scientists. Thus our contents are close to current research.


We thank the whole team involved in the implementation of the course. Besides Andreas Matt, Bianca Violet, Eric Londaits, Fabian Graap, Antonia Mey and Christian Stussak, this was Oliver Schön, Karla Traulsen, Daniel Ramos, Johanna Marschall, and Elisabeth Schaber.

The Explorables are based on exhibits from the exhibition “I AM A.I. - explaining artificial intelligence”, which was developed by IMAGINARY and funded by the Carl-Zeiss-Stiftung.

Dr. Andreas Matt
Dr. Andreas Matt
IMAGINARY gGmbH
Bianca Violet
Bianca Violet
IMAGINARY gGmbH
Eric Londaits
Eric Londaits
IMAGINARY gGmbH
Dr. Fabian Gaap
Dr. Fabian Gaap
Friedrich-Schiller-Universität Jena
Dr. Antonia Mey
Dr. Antonia Mey
University of Edinburgh
IMAGINARY gGmbH
Dr. Christian Stussak
Dr. Christian Stussak
IMAGINARY gGmbH

The creators of the learning opportunities are responsible for their content.

What else should I know?

Learning format:
Online course
Certification:
Confirmation of Participation
Level:
Beginner
License:
CC-BY-NC-SA 4.0