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• W3200010478 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. Supplemental Material: Supplemental material is available at https://doi.org/10.1287/ited.2021.0256 ." @default.
• W3200010478 created "2021-09-27" @default.
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• W3200010478 date "2023-01-01" @default.
• W3200010478 modified "2023-09-24" @default.
• W3200010478 title "Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course" @default.
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• W3200010478 doi "https://doi.org/10.1287/ited.2021.0256" @default.
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