ECON3389
Machine Learning for Economics
Large scale datasets, both in length (number of observations) and width (number of variables) have become ubiquitous across many applied areas. The goal of this course is to provide an introduction to methods that allow us to use these datasets for forecasting and classification, as well as for simplifying these very wide datasets with dimensionality reduction methods. The course will also examine how to explore network connectivity with such long and wide datasets. The course will make use of computational libraries from Python, Julia, or Matlab, but students are free to make use of other coding methods, such as R or Stata.
Course overview
- Department
- Economics
- School
- MCAS
- Credits
- 3
Requirements fulfilled
No source-backed degree requirement is attached to this course yet.
Official evaluation summary
3.61 / 5
Data freshness
Instructors
Sections
- Section 01
- Section 01