Spring 2027

Applications of Machine Learning in Economics

Applications of Machine Learning in Economics covers ML techniques integrated with econometrics for empirical research, including supervised/unsupervised learning and causal inference.

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Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 3

Recent recorded grades — Fall 2025: 4.00 GPA, 100.0% A/AB (n=8 letter grades).

What you’ll be able to do

  • Summarize the basic concepts of machine learning from an economist’s perspective, and how these tools complement the econometrics toolkit

  • Apply machine learning methods to real economic data

  • Evaluate the advantages and disadvantages of alternative machine learning algorithms in economic applications

Source: UW Guide

Grade history

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average GPA
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Prerequisites

Course map

ECON 725

ECON 726

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Professors

Spring 2027

No instructors recorded for this selection.

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Calendar & sections

Spring 2027

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Student experience

the class

Recent recorded grades — Fall 2025: 4.00 GPA, 100.0% A/AB (n=8 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • ML vs Econometrics: goals, settings, and tools
  • Supervised/Unsupervised Learning and Data Mining
  • Causal Inference, Policy Estimation, and Counterfactuals

Skills

  • Machine learning techniques for economics
  • Supervised and unsupervised learning methods
  • Causal inference and policy estimation methods
  • Practical application of ML and econometric techniques

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
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BC
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Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "099d15de60f12594ab7b73adbfe5bfb71815310bad1e48ea2b2eda817017e127",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}