在运筹学课程中引入和整合机器学习:一门应用驱动课程

Q3 Social Sciences INFORMS Transactions on Education Pub Date : 2023-01-01 DOI:10.1287/ited.2021.0256
J. Boutilier, Timothy C. Y. Chan
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引用次数: 4

摘要

人工智能(AI)和运筹学(OR)长期以来因其协同关系而交织在一起。鉴于人工智能尤其是机器学习的日益普及,我们面临着学生对这一领域教育产品日益增长的需求。本文介绍了两门向本科生(主要是工业工程和运筹学学生)介绍机器学习概念的课程。这些课程没有采取方法优先的方法,而是使用真实世界的应用程序来激励、介绍和探索这些机器学习技术,并强调与运筹学的有意义的重叠。大量的编程实践经验用于培养学生对这些技术的熟练掌握。学生反馈表明,这些课程极大地提高了学生对机器学习的兴趣,并提高了他们对分析可能产生的现实世界影响的认识,并帮助学生发展了他们可以应用的实践技能。我们相信,将机器学习和运筹学联系起来的类似应用驱动课程将是本科生OR课程的宝贵补充。补充材料:补充材料可在https://doi.org/10.1287/ited.2021.0256。
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Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course
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 .
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来源期刊
INFORMS Transactions on Education
INFORMS Transactions on Education Social Sciences-Education
CiteScore
1.70
自引率
0.00%
发文量
34
审稿时长
52 weeks
期刊最新文献
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