Theoretical Foundations of Large-scale Machine Learning
Recorded 2026-09-07 Fall 2026
Theoretical Foundations of Large-scale Machine Learning
Explores mathematical foundations of large-scale machine learning and optimization, focusing on algorithmic design tradeoffs.
Recorded instructors · Fall 2026 No instructors listed
Summary
1 / 5
Historical reviews of Dimitris Papailiopoulos: Reviewers describe the workload as requiring almost no effort to achieve an easy A.
Grade history
↗ average GPA
letter grades
letter grades
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All recorded terms · compare terms & instructors
Prerequisites
Course mapCOMP SCI/E C E 761
ECE 826
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
Professors
Fall 2026No instructors recorded for this selection.
Historical instructors & teaching patterns
Historical instructor Dimitris Papailiopoulos is described as a nice guy who offers an easy A with minimal effort. However, reviewers note that the technical quality of the class is low and students should not expect to learn useful material.
DIMITRIOS PAPAILIOPOULOS is recorded teaching in Spring 2022, Spring 2024. 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 2026Schedule 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
Historical reviews of Dimitris Papailiopoulos: The course offers an easy grade with minimal effort, but reviewers warn that the technical quality is low and little useful material is learned.
Recent recorded grades — Spring 2022: 3.39 GPA, 78.6% A/AB (n=14 letter grades); Spring 2024: 3.86 GPA, 94.4% A/AB (n=18 letter grades).
difficulty & workload
Historical reviews of Dimitris Papailiopoulos: Reviewers describe the workload as requiring almost no effort to achieve an easy A.
Historical reviews of Dimitris Papailiopoulos: Students report low technical quality and find the course content not useful for learning.
Topics
Skills
Grades
Latest available · Spring 2024— not enough history to project Fall 2026.
Grade distribution · % of letter grades
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AB
B
BC
C
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F
Grades over time
Through Spring 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
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
Raw records
[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:320:026052",
"course_id": "ECE 826",
"course_uid": "course_4870953907b88e074505beb7",
"term_id": "1272",
"source_course_id": "026052",
"source_subject_id": "320",
"title": "Theoretical Foundations of Large-scale Machine Learning",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Not Applicable"
}
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Madgrades
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel 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": "9f7c4b2d8350bf6bab700e062c4ec7cfccec226d237f681da09470a9fdd66f84",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}