ECON 5210 — Machine Learning in Economics
4 semester hoursGraduateLectureusually offered: springtypical days: T/FBostonTraditional
Offers an in-depth introduction to a broad range of machine learning concepts and methods. Addresses the growing importance of machine learning in fostering the development of new research topics. Uses algorithms to discover and explore data patterns and applies machine learning techniques to uncover variables to help answer economic questions. Covers assumptions that underlie machine learning methods and how they can be validated with economic data. Identifies appropriate and optimal machine learning models for economic datasets and compares machine learning methods with traditional economic models. Offers students an opportunity to develop practical machine learning skills from an economic perspective. Emphasizes practical and innovative empirical applications in research and business.
Prerequisites
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2026 | 2 | 21 | 50 | 42% | 14.5 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 48% · T 52% · W 48% · Th 0% · F 52%
Common patterns: TF (52% of sections), MW (48% of sections) — in patterns, R means Thursday
Professors
Spring
- Shuo Zhang (52% of students) · reviews
- Jianfei Cao (48% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (ECON course descriptions) · All ECON courses · Plan it at numap.app