Fall 2026

Introduction to Random Signal Analysis and Statistics

ECE 331 introduces random signal analysis and statistics, covering probability, random processes, and spectral analysis.

offering recorded3 credits
Recorded instructors · Fall 2026 Ramya Korlakai Vinayak

Summary

1 / 6

Exams are poorly designed with high-stakes questions, and the course covers too many topics with insufficient depth, creating a stressful workload.

Grade history

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average GPA
letter grades
A
AB
B
BC
C
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All recorded terms · compare terms & instructors

Prerequisites

Course map

(E C E 203 or 330) or member of Engineering Guest Students

ECE 331 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Fall 2026

Ramya Korlakai Vinayak is described as very knowledgeable but a bad teacher. Reviewers cite terrible prerequisites, mandatory lectures that do not cover much material, and insufficient office hours as major concerns.

Students report that her mini-lectures fail to clarify complex statistics and waste time. The course structure is criticized for covering too many topics with no depth, and exams are described as poorly designed and stressful.

Recent recorded grades — Fall 2022: 2.60 GPA, 22.2% A/AB (n=45 letter grades); Fall 2023: 3.18 GPA, 56.7% A/AB (n=30 letter grades); Fall 2025: 3.13 GPA, 48.9% A/AB (n=45 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Ramya Korlakai Vinayak is the current instructor, though no specific reviews are provided for her teaching. Historical reviews for Akbar Sayeed, Dimitris Papailiopoulos, and Eduardo Arvelo show mixed experiences regarding lecture style and helpfulness, with some praising their support and others criticizing relevance or engagement.

AKBAR SAYEED is recorded teaching in Fall 2012, Fall 2013. Recorded history may be incomplete and does not establish a future schedule.

DIMITRIOS PAPAILIOPOULOS is recorded teaching in Fall 2019, Fall 2020, Fall 2021, Fall 2022. Recorded history may be incomplete and does not establish a future schedule.

EDUARDO ROMERO ARVELO is recorded teaching in Fall 2024. Recorded history may be incomplete and does not establish a future schedule.

RAMYA KORLAKAI VINAYAK is recorded teaching in Fall 2022, Fall 2023, 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.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction27 / 550

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

Ramya Korlakai Vinayak is described as knowledgeable but a poor instructor, with reviews citing confusing lectures, insufficient office hours, and a stressful, poorly structured course experience.

Recent recorded grades — Fall 2023: 3.18 GPA, 56.7% A/AB (n=30 letter grades); Fall 2024: 2.97 GPA, 40.8% A/AB (n=71 letter grades); Fall 2025: 3.13 GPA, 48.9% A/AB (n=45 letter grades).

difficulty & workload

Exams are poorly designed with high-stakes questions, and the course covers too many topics with insufficient depth, creating a stressful workload.

Students find lectures unhelpful and mandatory, with explanations that fail to clarify complex statistics, leading to frustration with the flipped classroom format.

Topics

  • Probability, random variables, and random processes
  • Confidence intervals, experimental design, and hypothesis testing
  • Statistical averages, correlation, and spectral analysis
  • Random signals and noise in linear systems

Skills

  • Understanding probability, random variables, and random processes.
  • Applying confidence intervals, experimental design, and hypothesis testing.
  • Performing statistical averages, correlation, and spectral analysis.
  • Analyzing random signals and noise in linear systems.

Grades

Historical instructor

Fall 2026 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

5 earlier forecasts · 0.26 GPA average error.

Grades over time

Through Fall 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1320 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.

Download part 1
Selected offering source records
ECE 331 · Fall 2026

Introduction to Random Signal Analysis and Statistics

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": "1272:320:006326",
    "course_id": "ECE 331",
    "course_uid": "course_c6592733bd6855f32188cfa7",
    "term_id": "1272",
    "source_course_id": "006326",
    "source_subject_id": "320",
    "title": "Introduction to Random Signal Analysis and Statistics",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  }
]
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 2026-09-07

Download the original dataset
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": "772494ab550488697baebf3af34b955f16563a436adee46429e160ced6c63dcd",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}