Integrating machine learning concepts into undergraduate classes

TL;DR

This paper compares two methods of teaching machine learning concepts to undergraduate electrical engineering students, highlighting their effectiveness and student preferences to improve educational strategies in this emerging field.

Contribution

It introduces and evaluates two distinct teaching approaches for machine learning in undergraduate curricula, providing insights into their relative effectiveness and student preferences.

Findings

Students prefer the side-by-side teaching approach.

Workshop approach may be more effective for learning.

Preliminary assessments show improved student understanding.

Abstract

In this innovative practice work-in-progress paper, we compare two different methods to teach machine learning concepts to undergraduate students in Electrical Engineering. While machine learning is now being offered as a senior-level elective in several curricula, this does not mean all students are exposed to it. Exposure to the concepts and practical applications of machine learning will assist in the creation of a workforce ready to tackle problems related to machine learning, currently a hot topic in industry. Preliminary assessments indicate that this approach promotes student learning. While students prefer the proposed side-by-side teaching approach, numerical comparisons show that the workshop approach may be more effective for student learning, indicating that further work in this area is required.