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

INFORMS Transactions on Education

article

Justin J. Boutilier¹  Timothy C. Y. Chan² 

[1] Department of Industrial and Systems Engineering, University of Wisconsin–Madison;Department of Mechanical and Industrial Engineering, University of Toronto

关键词: machine learning;    prescriptive analytics;    teaching modeling;    active learning;   

DOI  :  10.1287/ited.2021.0256

学科分类：数学（综合）

来源: INFORMS

【 摘 要 】

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.

【 授权许可】

CC BY|CC BY-SA|CC BY-ND|CC BY-NC|CC BY-NC-SA|CC BY-NC-ND