ORIE 5751: Learning and Decision Making From Data


Time: Mondays and Wednesdays 15:20–16:35
Location: Bloomberg Center 161


Nathan Kallus
Office hours by appointment

Course website


Course overview

This course covers the analysis of data for making decisions with applications to electronic commerce, AI and intelligent agents, business analytics, and personalized medicine. The focus of the class is on how to make sense of data and use it to make better decisions using summarization, visualization, statistical inference, interaction, and supervised and reinforcement learning; on a framework for both conceptually understanding and practically assessing generalization, causality, and decision making using statistical principles and machine learning methods; and on how to effectively design intelligent decision-making systems. Topics include summarizing, visualizing, and comparing data distributions; drawing inferences and generalizing conclusions from data; making inferences about causal effects; A/B testing; instrumental variable analysis; sequential decision making and bandits; Markov decision processes; reinforcement learning; and ethics of data-driven decisions. Students are expected to have working knowledge of calculus, probability, and linear algebra as well as a modern scripting language such as Python or R.

Selection of topics covered


Linear algebra and calculus at the level of Math 1920 and 2940, probability at the level of ORIE 3500 or ENGRD 2700.

Grade breakdown

Suggested reading

There is no required textbook for the course. Lecture notes will be distributed with each lecture. Students are encouraged, but not required, and might find it useful to use the following books as additional reading resources: