Integrating machine learning concepts into undergraduate classes

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. To this end, the authors are working on introducing Electrical Engineering students to machine learning in a required, Junior-level, Signals and Systems course. The main challenge with teaching machine learning in a junior-level course involves requiring students to appreciate the linkages between complex concepts in linear algebra, statistics, and optimization. While it can be argued that Junior-level students should have seen concepts in some of these topics, requiring them to apply these topics together is a challenge. Therefore, in order to assist students to better grasp these concepts, we provide them with hands-on activities, since immersive experiences will help students appreciate the practical uses of machine learning. In a previous approach, authors held stand-alone workshops where students in the class were given Android apps for data collection, followed by different sets of hands-on activities. While this approach showed promise, several students indicated that the stand-alone workshop lacked context. To alleviate these concerns, in the Fall semester of 2020, the authors tried a different approach. Students were provided hands-on activities side-by-side with regular course content enabling links to be made with machine learning throughout the course and providing better context to the content being presented. 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.