Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course

Justin J. Boutilier () and Timothy C. Y. Chan ()
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Justin J. Boutilier: Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53706
Timothy C. Y. Chan: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 1A1, Canada

INFORMS Transactions on Education, 2023, vol. 23, issue 2, 64-83

Abstract: Artificial intelligence (AI) and operations research (OR) have long been intertwined because of their synergistic relationship. Given the increasing popularity of AI and machine learning in particular, we face growing demand for educational offerings in this area from our students. This paper describes two courses that introduce machine learning concepts to undergraduate, predominantly industrial engineering and operations research students. Instead of taking a methods-first approach, these courses use real-world applications to motivate, introduce, and explore these machine learning techniques and highlight meaningful overlap with operations research. Significant hands-on coding experience is used to build student proficiency with the techniques. Student feedback indicates that these courses have greatly increased student interest in machine learning and appreciation of the real-world impact that analytics can have and helped students develop practical skills that they can apply. We believe that similar application-driven courses that connect machine learning and operations research would be valuable additions to undergraduate OR curricula broadly.

Keywords: machine learning; prescriptive analytics; teaching modeling; active learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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