Linear Randomized Algorithms for Data Science
Covers randomized algorithms for linear data science problems, including mathematical foundations, computational analysis, and implementation in Julia.
Summary
Recent recorded grades — Spring 2023: 4.00 GPA, 100.0% A/AB (n=7 letter grades); Spring 2024: 4.00 GPA, 100.0% A/AB (n=6 letter grades).
What you’ll be able to do
Apply common (constructive and inductive) thinking and reasoning patterns used in randomized algorithms for deriving solutions to linear problems arising in data science
Transform constructive mathematical reasoning patterns into numerical algorithms, and criticize the computational resource utilization of these algorithms
Implement numerical algorithms using the Julia programming language, and become familiar with assistive tools such as debuggers and profilers
Create and implement rigorous numerical experiments to test and compare different algorithms and implementations, and draw appropriate conclusions from the results of these experiments
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapConsent of instructor
- Consent of instructor
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Consent of instructor
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
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
Student experience
the class
Recent recorded grades — Spring 2023: 4.00 GPA, 100.0% A/AB (n=7 letter grades); Spring 2024: 4.00 GPA, 100.0% A/AB (n=6 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Randomized algorithms for modern data science.
- Mathematical foundations of randomized algorithms.
- Computational resource utilization analysis.
- Linear problems in data science.
Skills
- Applying constructive and inductive reasoning patterns to derive solutions for linear problems in data science.
- Transforming mathematical reasoning into numerical algorithms and analyzing their computational resource usage.
- Implementing numerical algorithms in Julia and using debuggers and profilers.
- Creating and implementing numerical experiments to test and compare algorithms.
Grades
Latest available · Spring 2024— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2024
More grade details Grade mix, volume & source data
Sources & history
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
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
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": "656a0ba18486890d0250a5afd4508e5cde64b13a8498fc5654aa1ef6cfe8d9e6",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}