Andrew Zieffler (University of Minnesota), Chelsey Legacy (University of Minnesota), Regina Lisinker (University of Minnesota), Pablo Vivas Corrales (University of Minnesota)
Abstract
Have you ever found yourself wishing you could introduce ideas from machine learning into your statistics or mathematics classroom but weren’t sure how to do it? If so, this might be the session for you.
Through a set of hands-on activities drawing from a variety of engaging contexts, we will explore a common machine learning algorithm for classification. Drawing on mathematical concepts (e.g., algebra, trigonometry), these activities can help build a foundation for students’ algorithmic thinking. This session will also include a discussion of how and where these activities could be implemented in participants’ statistics or mathematics courses.