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ECON 490 E3 - Topics in Economics - Applied Machine Learning: Econ

Campus: Urbana-Champaign


Special topics in advanced economics within a variety of areas. See course schedule for topics. Course Information: 3 undergraduate hours. 4 graduate hours. May be repeated in the same or separate terms to a maximum of 9 undergraduate hours or 8 graduate hours if topics vary. Prerequisite: ECON 202; ECON 302 or ECON 303; MATH 220 or MATH 221 or other Calculus course. Some topics may require additional prerequisites, read the section text for each topic.

Special Instructions:

**RESTRICTION INFO: https://go.economics.illinois.edu/SpringRestrictionsApplied **DESCRIPTION: This course gives an overview of different concepts, techniques, and algorithms in machine learning and their applications in economics. We begin with topics such as classification, linear and non-linear regressions and end with more recent topics such as boosting, support vector machines, and Neural networks as time allows. This course will give students the basic knowledge behind these machine learning methods and the ability to utilize them in an economic setting. Students will be led and mentored to develop and solve an economic problem with machine learning methods introduced during the course. **PREREQUISITES: ECON 203 & 302, MATH 220/221 Required; ECON 471 or Econometrics knowledge Highly Recommended (we will expect you to be comfortable with econometrics).

Option 1

Number of Required Visit(s): 0

Course Level: Graduate

Credit: 3

Term(s): Fall , Spring


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