Teaching Basic Concepts in Machine Learning to Engineering Students: A Hands-on Approach

Abstract According to a recent survey conducted by the Corporate Member Council of the American Society of Engineering Education (ASEE), there exists a notable disparity in the skillset of engineering graduates in relation to Artificial Intelligence (AI). To address this, the Africa Centre of Excellence on New Pedagogies in Engineering Education organized a machine learning (ML) workshop for engineering students from different disciplines. Seventy-three (73) students enrolled for the workshop and the modules covered during this workshop were: Introduction to ML Models, ML Frameworks, Additive Explanations in ML, Performance Metrics, and Introduction to Ensemble Learning Techniques. The hands-on session involved the use of categorical boosting, an ensemble learning technique, to predict the mechanical properties of perovskite materials. The survey results indicate that the learning modules are an effective introduction for novice engineering students in this domain and raise awareness of the importance of this important sub-section of AI.