ECON 3389 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
- Level
- Undergraduate
- Offered
- Every Spring
Catalog details
- Prerequisites
- ECON1151
Requirements fulfilled
- Economics B.A.: Economics elective options (Current University Catalog; students should confirm their catalog year)
Official evaluation summary
3.61 / 5
Data freshness
Instructors
- Matteo Masullo
- Paul D Mcnelis
- Anatoly Arlashin
- Anshuman Bhakri
- Arnab Palit
- Haydar Evren
- Pietro Visaggio
- Shane Mcmiken
- Yunus Semih Coskun
Sections
- Section 01
- Section 01