Capstone Project in Machine Learning and Signal Processing
A capstone project course where students apply machine learning and signal processing concepts to individual or team projects.
Summary
What you’ll be able to do
Identify a real-world problem that can be addressed and answered using techniques in machine learning and signal processing
Think critically about the end-to-end formulation of a real-world machine learning and signal processing task
Apply MLSP concepts to a real-world machine learning and signal processing task
Ask and answer deep and driving questions about a machine learning and signal processing project
Gain, sharpen, and showcase skills in teamwork, problem solving, reflection, and communication
Source: UW Guide
Grade history
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Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
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
Individual or team project to gain hands-on-experience applying machine learning and signal processing concepts.
difficulty & workload
No workload feedback recorded.
Topics
- Machine learning and signal processing
- Real-world problem identification
- End-to-end task formulation
Skills
- Identifying real-world problems addressable with ML and signal processing
- Critical thinking for end-to-end task formulation
- Applying MLSP concepts to real-world tasks
- Asking and answering deep project-specific questions
- Teamwork, problem solving, reflection, and communication
Grades
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Sources & history
Catalog & offerings
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Raw records
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Student reviews
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "b55e96dd12725fdc7a40383a4039052258aa4e53f84ecc7d466f7bac6ad9b2a0",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}