Probability and Random Processes

ECE 730 covers advanced probability concepts and random processes, including filtering, spectral densities, and stochastic processes.

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

Summary

1 / 5

Historical reviews of John Gubner: The course is mathematically rigorous with few physical examples, requiring significant self-study from textbooks and precise understanding of fundamentals to succeed.

Grade history

average GPA
letter grades
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Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
ECE 730

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

Prerequisite text tree
  • Graduate/professional standing

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews for John Gubner describe a graduate-level course with a rigorous, mathematical approach that prioritizes theory and proofs over physical intuition or applications. While reviewers note he is prepared, patient, and willing to clarify doubts, his lectures are often described as dry and difficult to follow, requiring significant self-study from textbooks.

JOHN GUBNER is recorded teaching in Spring 2009, Spring 2011, Fall 2012, Fall 2013, Fall 2014, Fall 2015, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022. 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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Meeting source records

No records available.

Student experience

the class

Historical reviews for John Gubner describe a difficult graduate course with dry lectures and tough grading, though he is noted as helpful and patient with student questions.

Recent recorded grades — Fall 2022: 3.50 GPA, 70.6% A/AB (n=17 letter grades); Fall 2023: 4.00 GPA, 100.0% A/AB (n=7 letter grades); Fall 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades).

difficulty & workload

Historical reviews of John Gubner: The course is mathematically rigorous with few physical examples, requiring significant self-study from textbooks and precise understanding of fundamentals to succeed.

Historical reviews of John Gubner: Students find lectures dry and hard to follow, often relying on textbooks, while others appreciate his willingness to answer questions patiently despite the challenging material.

Topics

  • Random vectors, linear filtering, stationarity, power spectral densities, estimation, convergence, Markov chains, Poisson process, Wiener process.

Skills

  • Advanced probability concepts including random vectors, linear filtering, stationarity, power spectral densities, estimation, convergence, Markov chains, Poisson process, and Wiener process.

Grades

Latest available · Fall 2024— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
B
BC
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Grades over time

Through Fall 2024

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

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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.

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Grade history

Recorded grade distributions by term, section, and instructor.

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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": "2b0918aea962cd9b6aadb10c648fe7a53dbbe9701407ea3cb14050b7502b588b",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}