Spring 2027

Machine Learning for Data-driven Engineering Design

Introduction to machine learning and data-driven methods for surrogate modeling and design optimization applied to structural, thermofluid, materials, manufacturing, energy, and complex system engineering design problems.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 2

Recent recorded grades — Fall 2024: 3.35 GPA, 45.0% A/AB (n=20 letter grades); Fall 2025: 3.88 GPA, 100.0% A/AB (n=8 letter grades).

What you’ll be able to do

  • Identify concepts and techniques for machine learning and data-driven engineering design

  • Demonstrate skills in constructing surrogate models for design problems

  • Demonstrate skills in applying machine learning and data-driven models in design problems

  • Apply the machine learning and data-driven methods to solve engineering design problems

  • Identify mathematics for machine learning and data-driven engineering design

Source: UW Guide

Grade history

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average GPA
letter grades
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AB
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All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 234 or 376), (MATH 320, 340, 341, or 375), and (I SY E 210,B M E 325,E C E 331,MATH/​STAT 310, 410,STAT 312, 324, 333 or 340), or graduate/professional standing

ME 536

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

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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

Calendar & sections

Spring 2027

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Student experience

the class

Recent recorded grades — Fall 2024: 3.35 GPA, 45.0% A/AB (n=20 letter grades); Fall 2025: 3.88 GPA, 100.0% A/AB (n=8 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Machine learning and data-driven methods for surrogate modeling and design optimization
  • Applications to structural, thermofluid, materials, manufacturing, energy, and complex system engineering design

Skills

  • Identify machine learning concepts and techniques for data-driven engineering design
  • Construct surrogate models for engineering design problems
  • Identify mathematical foundations for machine learning and data-driven design

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

A
AB
B
BC
C
D
F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

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

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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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": "2e04e036701fe1356d06e2912c87126351955eaf2cb473c1903103f9d664e92d",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}