ORIE 5751 - Learning, Inference, and Decision Making from Data

(crosslisted) CS 5726

Spring. 3 credits. Student option grading.

Prerequisite: ORIE 5750 or CS 5785 and working knowledge of calculus, probability, and linear algebra as well as a modern scripting language such as Python. Enrollment limited to: Cornell Tech students. Offered in New York City at Cornell Tech.

N. Kallus.

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 will be on learning good and automated decision policies, inferring causal effects of potential decisions, and interactive and intelligent systems that learn through acting and act to learn. Topics include A/B testing, sequential decision making and bandits, decision theory, risk minimization and generalization, Markov decision processes, reinforcement learning, analysis of observational data, instrumental variable analysis, and algorithmic fairness of personalized decision policies. Students are expected to have taken a first course in machine learning and have working knowledge of calculus, probability, and linear algebra as well as a modern scripting language such as Python.