Integrating machine learning concepts into undergraduate classes

• URL: http://arxiv.org/abs/2211.06491v1
• Date: Wed, 9 Nov 2022 23:28:45 GMT
• Title: Integrating machine learning concepts into undergraduate classes
• Authors: Chinmay Sahu, Blaine Ayotte, Mahesh K. Banavar
• Abstract summary: Machine learning is now being offered as a senior-level elective in several curricula. 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. While students prefer the proposed side-by-side teaching approach, numerical comparisons show that the workshop approach may be more effective for student learning.
• Score: 4.917075909999549
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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