WHAT UNIVERSITY-LEVEL MATHEMATICS NEEDED TO UNDERSTAND DEEP LEARNING CAN BE MADE ACCESSIBLE IN HIGH SCHOOL?

Martin Frank

Keywords: AI and Data Science Curricula and Implementation in School

Short Summary: In this contribution, we would like to analyze the university-level mathematics that the research community currently believes to be crucial for the mathematical understanding of when, how and why supervised deep learning works (Kutyniok, 2024), with respect how much of it (“how much” certainly needs to be specified) can in principle be taught in high school. The analysis assumes that opening the black box of AI and thereby demystifying it empowers students as individual human beings (German Mündigkeit). We focus on the non-statistical aspects, not because the statistical aspects are not important, but because they are covered better in other talks at the workshop.

Topic Area: AI and Data Science Curricula and Implementation in School

Presenting Author: Martin Frank

Presentation Type: Short Presentation (ca. 20 min)

Submission Number: 33