Fall 2026

Applications of Machine Learning in Economics

ECON 726 teaches the application of machine learning techniques to economic research, focusing on causal inference and policy estimation.

offering recorded3 credits

Recorded instructors · Fall 2026 Yong Cai3.7/5 Jack Porter3.5/5

Summary

1 / 2

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

Grade history

↗

average GPA
letter grades

All recorded terms · compare terms & instructors

Course details

Credits3 credits

Typically offeredNot Applicable

Subjects

Economics

About this course

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.

Prerequisites

Course map

ECON 725

ECON 725

ECON 726

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

ECON 725

Professors

Fall 2026

Yong Cai

/5Adjusted rating

/5RMP difficulty

captured reviews

About this rating

Raw average: 5.00/5 from 1 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

No course-specific feedback yet.

Jack Porter

/5Adjusted rating

/5RMP difficulty

captured reviews

About this rating

Raw average: 3.40/5 from 15 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

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

Historical instructors & teaching patterns

JACK PORTER is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Fall 2026

Schedule loads here as you scroll.

Section	Mode	Enrolled / capacity	Waitlist
LEC 001	Classroom Instruction	17 / 20	0

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records

Download part 1

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

Supervised and unsupervised learning methods, large data analysis, and data mining.
Comparison of goals, empirical settings, and tools between machine learning and econometrics.

Skills

Application of machine learning techniques in economic contexts.
Practical application of ML tools to answer economic research questions.
Use of ML methods for causal inference, optimal policy estimation, and counterfactual effect estimation.

Grades

Latest available · Fall 2025— not enough history to project Fall 2026.

Grade distribution · % of letter grades

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1

Selected offering source records

ECON 726 · Fall 2026

Applications of Machine Learning in Economics

Recorded 2026-09-07

Raw records

[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:296:027025",
    "course_id": "ECON 726",
    "course_uid": "course_01ccf4de474c3ea235009a48",
    "term_id": "1272",
    "source_course_id": "027025",
    "source_subject_id": "296",
    "title": "Applications of Machine Learning in Economics",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]

Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades

Model outputs & technical records

nvidia/Qwen3.6-35B-A3B-NVFP4

LLM outputs across runs

Download part 1

Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1

Model & dataset provenance

{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "7cea056c365766876c2c163944ba3c7f302a067302efe3e9e7a0ce8bfeea6db0",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}