Spring 2027

Theoretical Foundations of Large-scale Machine Learning

Theoretical foundations of large-scale machine learning covering mathematical optimization, randomized algorithms, and tradeoffs like convergence speed and scalability.

offering recorded3 credits
Recorded instructors · Spring 2027 Ramya Korlakai Vinayak

Summary

1 / 6

Past student reviews · Dimitris Papailiopoulos

A previous student reported receiving an A with almost no effort, indicating very low difficulty and workload demands.

What you’ll be able to do

  • Prove convergence rates for stochastic optimization algorithms

  • Describe systems tradeoffs that drive algorithmic design in large-scale machine learning

  • Summarize recent literature in large-scale optimization and machine learning

  • Design, tune, or tailor a given machine learning algorithm for a new system

  • Evaluate statements about parallelizability, generalization, convergence of optimization algorithms

Source: UW Guide

Grade history

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

Prerequisites

Course map

COMP SCI/​E C E 761

ECE 826

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

Prerequisite text tree

Professors

Spring 2027
Historical instructors & teaching patterns

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

Spring 2027

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SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 300

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 · Dimitris Papailiopoulos

Past students described the course as easy to pass with minimal effort, though they felt the technical quality was low and the learning experience was not useful.

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

Past student reviews · Dimitris Papailiopoulos

A previous student reported receiving an A with almost no effort, indicating very low difficulty and workload demands.

Past student reviews · Dimitris Papailiopoulos

While the instructor was considered nice, the student found the class technically lacking and did not feel they learned anything useful from the course.

Topics

  • Large-scale machine learning and optimization
  • Randomized algorithms
  • Algorithmic design tradeoffs

Skills

  • Proving convergence rates for stochastic optimization algorithms
  • Describing systems tradeoffs driving algorithmic design
  • Designing and tuning machine learning algorithms for new systems

Grades

Latest available · Spring 2024— 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
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Grades over time

Through Spring 2024

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 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.

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Selected offering source records
ECE 826 · Spring 2027

Theoretical Foundations of Large-scale Machine Learning

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": "1274:320:026052",
    "course_id": "ECE 826",
    "course_uid": "course_4870953907b88e074505beb7",
    "term_id": "1274",
    "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.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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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.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "f74bad4d43f3a02b5d3d6d55d58ba384c3672b0c40429a1de9f2c209b93b8632",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}