This Ph.D. level course will provide an overview of machine learning and its algorithmic paradigms, and explore recent topics on learning, inference, and decision-making with large data sets. Emphasis will be made on theoretical insights and algorithmic principles.

This 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.