Spring 2027

Stochastic Programming

Covers stochastic programming for decision making under uncertainty, including modeling, risk measures, algorithms, and sampling methods.

offering recorded3 credits
Recorded instructors · Spring 2027 Jim Luedtke3.9/5

Summary

1 / 6

Past student reviews · Jim Luedtke

Assignments were designed to reinforce understanding of class material, and the course received a high difficulty rating.

What you’ll be able to do

  • Learn the terms, basic capabilities, and limitations of stochastic programming models

  • Formulate stochastic programming models

  • Implement the algorithms used to solve stochastic programming problems

  • Learn principles of decomposition algorithms for solving large-scale optimization problems

Source: UW Guide 1 UW Guide 2

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI/ISYE 719

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

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.62/5 from 8 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.

For this course: 5.0/5 raw quality · 4.0/5 difficulty · 1 reviews

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

Past student reviews · Jim Luedtke

Jim Luedtke delivered well-structured lectures that balanced applications and math. His assignments helped students understand class material, and one student highly recommended the course.

Historical instructors & teaching patterns

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

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 300
LEC 001Classroom Instruction0 / 300

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

Meeting source records
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Student experience

the class

Past student reviews · Jim Luedtke

Students found Jim Luedtke’s lectures well-structured and balanced between applications and math, describing the course as one of the best they had taken.

Recent recorded grades — Spring 2020: 3.62 GPA, 69.2% A/AB (n=13 letter grades); Fall 2023: 3.78 GPA, 82.6% A/AB (n=23 letter grades); Spring 2025: 3.68 GPA, 70.6% A/AB (n=17 letter grades).

difficulty & workload

Past student reviews · Jim Luedtke

Assignments were designed to reinforce understanding of class material, and the course received a high difficulty rating.

Past student reviews · Jim Luedtke

The reviewer highly recommended the class, noting that the assignments effectively helped them understand the topics covered in lectures.

Topics

  • Modeling uncertainty, risk measures, stochastic programming algorithms, approximation and sampling methods, and applications

Skills

  • Formulating and understanding stochastic programming models
  • Implementing algorithms and decomposition principles for optimization

Grades

Latest available · Spring 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 Spring 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

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

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Selected offering source records
COMPSCI/ISYE 719 · Spring 2027

Stochastic Programming

Recorded 2026-10-29
COMPSCI/ISYE 719 · Spring 2027

Stochastic Programming

Recorded 2026-10-29
Raw records 
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    "course_id": "COMPSCI/ISYE 719",
    "course_uid": "course_8933a7eb8d4940ef2a994569",
    "term_id": "1274",
    "source_course_id": "004309",
    "source_subject_id": "266",
    "title": "Stochastic Programming",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Occasionally"
  },
  {
    "run_id": "20261029T213040-0fa85c20",
    "semester": "1274",
    "observed_at": "2026-10-29 21:30:40.248904+00:00",
    "offering_id": "1274:490:004309",
    "course_id": "COMPSCI/ISYE 719",
    "course_uid": "course_8933a7eb8d4940ef2a994569",
    "term_id": "1274",
    "source_course_id": "004309",
    "source_subject_id": "490",
    "title": "Stochastic Programming",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Occasionally"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-29

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "4fb43d89a49aad3714d69dec1a6237724e1bc67ee5550e0a4c864a74819e54f4",
  "requirements_status": "valid",
  "dataset_revision": "c9cd06eaa45f9ddf7e69faa38b89ff3d3dd995dd",
  "observed_at": "2026-10-29 21:30:40.248904+00:00"
}