Spring 2027

Digital Signal Processing

Covers sampling, reconstruction, DFT spectral analysis, FFT, z-transforms, FIR/IIR filter design, signal flow graphs, and filter implementation in digital signal processing.

offering recorded3 credits
Recorded instructors · Spring 2027 Paul Milenkovic2.6/5

Summary

1 / 6

Past student reviews · Robert Nowak

Homeworks ranged from time-consuming to nearly impossible, and tests were difficult. You had better know MATLAB really well, as it was expected.

What you’ll be able to do

  • Choose filter characteristics and parameters for sampling and interpolating signals

  • Relate pole and zero locations to system properties

  • Design discrete-time filters

  • Perform spectral analysis of signals

  • Perform signal processing operations using an engineering software package

Source: UW Guide

Grade history

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

Prerequisites

Course map

E C E 330, graduate/professional standing, or member of Engineering Guest Students

    • ECE 330
    • graduate/professional standing
    • member of Engineering Guest Students
    take one
ECE 431

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

Prerequisite text tree
  • Any of
    • ECE 330
    • graduate/professional standing
    • member of Engineering Guest Students

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.27/5 from 63 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.

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

Recent recorded grades — Spring 2024: 3.63 GPA, 77.8% A/AB (n=27 letter grades); Spring 2025: 3.48 GPA, 78.0% A/AB (n=50 letter grades); Spring 2026: 3.84 GPA, 97.7% A/AB (n=43 letter grades).

Historical instructors & teaching patterns

PAUL MILENKOVIC is recorded teaching in Fall 2020, Fall 2021, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

ROBERT NOWAK is recorded teaching in Fall 2006. 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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 450

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 · Robert Nowak

Students described the material as mostly boring but noted that lectures were imperative if you opted not to get the book.

Recent recorded grades — Spring 2024: 3.63 GPA, 77.8% A/AB (n=27 letter grades); Spring 2025: 3.48 GPA, 78.0% A/AB (n=50 letter grades); Spring 2026: 3.84 GPA, 97.7% A/AB (n=43 letter grades).

difficulty & workload

Past student reviews · Robert Nowak

Homeworks ranged from time-consuming to nearly impossible, and tests were difficult. You had better know MATLAB really well, as it was expected.

Past student reviews · Robert Nowak

One student found the generous final grading to be a saving grace, resulting in a B that made them really happy.

Topics

  • Sampling and reconstruction of continuous-time signals
  • Spectral analysis using the discrete Fourier transform
  • Fast Fourier transform and fast convolution methods
  • Z-transforms
  • FIR and IIR filter design techniques
  • Signal flow graphs and filter implementation

Skills

  • Sampling and reconstruction of continuous-time signals
  • Spectral analysis using the discrete Fourier transform
  • Fast Fourier transform and fast convolution methods
  • Z-transforms
  • Finite and infinite impulse response filter design
  • Signal flow graphs and filter implementation

Grades

Latest available · Spring 2026— 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 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

1280 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.

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Selected offering source records
ECE 431 · Spring 2027

Digital Signal Processing

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:320:006371",
    "course_id": "ECE 431",
    "course_uid": "course_a4ae993219c28eb4a4c5c7e2",
    "term_id": "1274",
    "source_course_id": "006371",
    "source_subject_id": "320",
    "title": "Digital Signal Processing",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall"
  }
]
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 2026-10-14

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": "7458887a97500df53d4d8e438430615ae5dafa1c3aa1193e81ec7e07ece0ad6a",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}