Fall 2026

Data-driven Dynamical Systems, Stochastic Modeling and Prediction

Introduction to data-driven dynamical systems, stochastic modeling, and prediction using numerical algorithms and programming.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 5

Historical reviews of Samuel Stechmann: The course is rated as having low difficulty, with a difficulty rating of 2.

Grade history

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

Prerequisites

Course map

(MATH 320, 340, 341, 345, or 375) and (STAT/​MATH 309, 431,STAT 311, or MATH 531) and (MATH 322, 341, 375, 421, or 467), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)

MATH 616

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

Prerequisite text tree

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews of Samuel Stechmann: Samuel Stechmann is described as an awesome professor who delivers funny, clear, and engaging lectures. Reviewers strongly recommend taking a class with him if the opportunity arises.

SAMUEL STECHMANN is recorded teaching in Fall 2024. 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

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SectionModeEnrolled / capacityWaitlist

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

Meeting source records

No records available.

Student experience

the class

Historical reviews of Samuel Stechmann: Samuel Stechmann delivers funny, clear, and engaging lectures, making his course highly recommended for those who have the opportunity to take it with him.

Recent recorded grades — Fall 2024: 3.57 GPA, 75.7% A/AB (n=37 letter grades); Spring 2026: 4.00 GPA, 100.0% A/AB (n=40 letter grades).

difficulty & workload

Historical reviews of Samuel Stechmann: The course is rated as having low difficulty, with a difficulty rating of 2.

Historical reviews of Samuel Stechmann: Students find the lectures engaging and clear, describing the professor as funny and highly effective.

Topics

  • Stochastic toolkits for dynamical systems and data science
  • Linear Gaussian processes
  • Nonlinear stochastic systems
  • Elementary stochastic differential equations
  • Data assimilation
  • Parameter estimation
  • Forecasting and prediction

Skills

  • Programming for data-driven systems
  • Numerical algorithms for dynamical systems
  • Stochastic toolkits for data science
  • Data assimilation and prediction

Grades

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

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 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1283 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

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.

Selected offering source records

No offering records for the selected term.

Raw records
[]
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Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "35f66f3e44746496adbb4c6054a7d1c44c8968a4c90c79865480acb5e528db84",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}