We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

MACHINE LEARNING FOR ECONOMISTS

ECON 725
Course Description

Introduction to the use of Machine Learning (ML) in economic analysis. Covers basic techniques of ML, much attention will be devoted to evaluating the use of these tools in economics. Learn how economists are integrating the tools of ML with econometric techniques in current empirical research. Gain hands on experience in using these techniques to answer traditional questions of interest to economists. Topics include (i) an in-depth discussion of the differences and similarities in goals, empirical settings and tools between ML and econometrics, (ii) supervised learning methods for regression and classification, unsupervised learning methods, large data analysis and data mining, (iii) recent methods at the intersection of ML and econometrics, designed for causal inference, optimal policy estimation, estimation of counterfactual effects. The methods are taught with an emphasis on practical application.

Prerequisties

Declared in an Economics graduate program

Satisfies
Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.74

2.61% from Historical

Completion Rate
100%

No change from Historical

A Rate
52.38%

18.28% from Historical

Class Size
42

-10% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

ADVANCES IN ARTIFICIAL INTELLIGENCE FOR ECONOMISTS

ECON 771

Introduction to the basic concepts of artificial intelligence and its use in economics. Covers general principles and specific economic applications. Discusses implementing these approaches using economic data.

ECONOMIC FORECASTING

ECON 460

Introduction to econometric time series analysis, forecasting methods, and forecast evaluation. Covers theoretical, methodological, and applied topics, and much of the work will be hands-on data analysis.

MACHINE LEARNING IN APPLIED ECONOMIC ANALYSIS

AAE 722

The basic methods, implementation and applications of machine learning for understanding contemporary economic issues using large data sets. Building upon understanding of standard econometric models, the topics include data mining techniques; regression model selection and regularization; post selection inference and economic applications; tree-based methods; neural networks; random forests and casual inference; and unsupervised learning.

TOPICS IN COMPUTATIONAL ECONOMICS

ECON 880

Covers techniques to compute and estimate structural models. Transition from formal Econometrics, Macroeconomics and Theory training to applications.

APPLICATIONS OF MACHINE LEARNING IN ECONOMICS

ECON 726

Techniques of Machine Learning (ML) and the use of these tools in economics. Exploration of how economists are integrating ML tools with econometric techniques in current empirical research. Hands on experience in using these techniques to answer traditional questions of interest to economists. Topics include (i) an in-depth discussion of the differences and similarities in goals, empirical settings and tools between ML and econometrics, (ii) supervised learning methods for regression and classification, unsupervised learning methods, large data analysis and data mining, (iii) recent methods at the intersection of ML and econometrics, designed for causal inference, optimal policy estimation, estimation of counterfactual effects. The methods are taught with an emphasis on practical application.