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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Justin J. Boutilier & Timothy C. Y. Chan, 2023. "Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 64-83, January.
    • Handle: RePEc:inm:orited:v:23:y:2023:i:2:p:64-83
    DOI: 10.1287/ited.2021.0256
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    References listed on IDEAS

    as
    1. Thomas Bortfeld & Timothy C. Y. Chan & Alexei Trofimov & John N. Tsitsiklis, 2008. "Robust Management of Motion Uncertainty in Intensity-Modulated Radiation Therapy," Operations Research, INFORMS, vol. 56(6), pages 1461-1473, December.
    2. Timothy C. Y. Chan & Tim Craig & Taewoo Lee & Michael B. Sharpe, 2014. "Generalized Inverse Multiobjective Optimization with Application to Cancer Therapy," Operations Research, INFORMS, vol. 62(3), pages 680-695, June.
    3. Ravindra K. Ahuja & James B. Orlin, 2001. "Inverse Optimization," Operations Research, INFORMS, vol. 49(5), pages 771-783, October.
    4. Ashenfelter, Orley, 2010. "Predicting the Quality and Prices of Bordeaux Wine," Journal of Wine Economics, Cambridge University Press, vol. 5(1), pages 40-52, April.
    5. Timothy C. Y. Chan & Douglas Fearing, 2019. "Process Flexibility in Baseball: The Value of Positional Flexibility," Management Science, INFORMS, vol. 65(4), pages 1642-1666, April.
    6. Lawrence H. Riddle, 1988. "Probability Models for Tennis Scoring Systems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 37(3), pages 490-490, November.
    7. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    8. Timothy C. Y. Chan & Justin A. Cho & David C. Novati, 2012. "Quantifying the Contribution of NHL Player Types to Team Performance," Interfaces, INFORMS, vol. 42(2), pages 131-145, April.
    9. Kate A. Smith, 1999. "Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research," INFORMS Journal on Computing, INFORMS, vol. 11(1), pages 15-34, February.
    10. Alex Chohlas-Wood & E. S. Levine, 2019. "A Recommendation Engine to Aid in Identifying Crime Patterns," Interfaces, INFORMS, vol. 49(2), pages 154-166, March.
    11. Lawrence H. Riddle, 1988. "Probability Models for Tennis Scoring Systems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 37(1), pages 63-75, March.
    12. Brendan K. Eagen & Timothy C. Y. Chan & Michael W. Carter, 2018. "Women’s College Hospital Uses Operations Research to Create an Ambulatory Clinic Schedule," Service Science, INFORMS, vol. 10(3), pages 230-240, September.
    13. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    14. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    15. Timothy C. Y. Chan & Derya Demirtas & Roy H. Kwon, 2016. "Optimizing the Deployment of Public Access Defibrillators," Management Science, INFORMS, vol. 62(12), pages 3617-3635, December.
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