Amazon, Netflix, Zalando, Alliance, Apple, Google, Facebook. The list should not be a surreptitious advertisement for one of the companies mentioned. All seven are companies that are active in various industries. But from social networks, insurance companies and technology production to online shopping and video streaming, they all have something in common. The common ground is responsible for the degree of awareness and influence of the corporations. It is a current field of research that offers countless applications and is used in a wide variety of areas. This is the field of artificial intelligence.

During this CAMMP day, the students learn about the application of artificial intelligence in the field of face recognition and autonomous driving. To this end, they first develop a method with which traffic lights can be automatically classified into different classes depending on their color. They then apply this method to the recognition of the faces of their fellow students. The aim of this workshop is to recognise the practical relevance of mathematics in everyday life and to establish that there is no need for huge research departments like the above-mentioned companies to use artificial intelligence.

In terms of content, the workshop can be classified in the field of analytical geometry.

Duration: 5 - 6 hours (incl. lunch break)
Contents: Vectors, scalar product, distance point straight/plane
Previous knowledge: vectors only
Target group: Upper secondary mathematics courses (ideally K1 & K2)
Created by: Lars Schmidt, Sarah Schönbrodt
Registration: Appointments can be made individually by e-mail at KIT or RWTH Aachen University.

Source of the image: https://pixabay.com/de/illustrations/tech-kreis-technologie-abstrakt-3041437/

• Schönbrodt, S.: Perspectives on teaching the Mathematics of Machine Learning to high-school students (presentation), ICTMA, Hong Kong, 2019.

• Schmidt, L.: Machine Learning: automatische Bilderkennung mit Mathematik?! - Ein Lehr-Lern-Modul im Rahmen eines mathematischen Modellierungstages für Schülerinnen und Schüler der Sekundarstufe II, Masterarbiet, RWTH Aachen, 2019.
• Schönbrodt, S.: Chancen für Machine Learning im Mathematikunterricht (Sek. II) (Vortrag Lehrerfortbildung), ISTRON, Würzburg, 2018.
• Schönbrodt, S.: Vergleich zweier Methoden zur Bildklassifizierung auf Basis maschineller Lernalgorithmen und ihre Anwendbarkeit in der Vermittlung mathematischer Modellierung, Master thesis, RWTH Aachen, 2018.