2024 ASEE Annual Conference & Exposition

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

Presented at Materials Division (MATS) Technical Session 1

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.

Authors
  1. Dr. David Olubiyi Obada Ahmadu Bello University, Nigeria [biography]
  2. Mr. Simeon Akindele Abolade Atlantic Technological University, Ireland [biography]
  3. Mr. Shittu Babatunde Akinpelu Atlantic Technological University, Ireland [biography]
  4. Ayodeji Nathaniel Oyedeji Ahmadu Bello University, Nigeria [biography]
  5. Dr. Emmanuel Okafor Orcid 16x16http://orcid.org/0000-0001-6929-6880 King Fahd University of Petroleum and Minerals, Saudi Arabia [biography]
  6. Ms. Cynthia Ujuh Odili Ahmadu Bello University, Nigeria [biography]
  7. Fatai Olukayode Anafi Ahmadu Bello University, Nigeria [biography]
  8. Abdulkarim Salawu Ahmed Ahmadu Bello University, Nigeria
  9. Dr. Akinlolu Akande Atlantic Technological University. Ireland [biography]
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