This PhD-level reading course develops research fluency in applied AI through close reading and discussion of research papers, with emphasis on identifying open problems and forming new research directions.

This PhD-level course provides an overview of machine learning and its algorithmic paradigms, and explores recent topics on learning, inference, and decision-making with large datasets. Emphasis is placed on theoretical insights and algorithmic principles.

This MBA-level course covers fundamental statistical concepts and basic computational tools in data analysis. The goal is to learn how to perform descriptive and predictive data analysis based on real datasets. This course also serves as a quantitative foundation for Chicago Booth elective courses in marketing, finance, economics and more advanced courses in data science.