An upper-level interdisciplinary course covering decision-making theory and associated real-world applications in the field of artificial intelligence and business analytics. This course addresses a crucial need in both the IEOR and CS departments for students to
study and get practice in methods that are quickly becoming pervasive in the industry.
In the first half of the course, students will receive methodical training in decision-making theory, dynamic programming, reinforcement learning, and modeling estimation. In the second half of the course, students will explore how these theories help data analysts model complicated decision-making processes and derive business values. This unique course offering will help students develop a solid mathematical foundation, strong programming and data analysis skills in Python, and the ability to apply decision-making models in real problems. The target audience of this course includes advanced undergraduate and graduate students with interests in
business analytics, computer science, and data science.
The proposed pedagogy and instruction for the course will leverage the expertise and teaching abilities of both P.I.s, who have have research and teaching expertise in artificial intelligence and business analytics, to make this course accessible to a broad interdisciplinary audience. This course will also serve as an elective for students in the relevant tracks of their respective majors.