Fall 2026

Mathematical Foundations of Machine Learning

COMPSCI/ECE 761 covers the mathematical foundations of machine learning, including probabilistic, algebraic, and geometric models, and the analysis of learning algorithms and optimization methods.

no offering record for this termCredits unavailable

Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 5

Historical reviews of Grigoris Chrysos, Robert Nowak: The course is a graduate-level, proof-based class on mathematical foundations of ML. Reviewers report high difficulty, noting that a strong statistics background is required but often not clearly explained, and that exams are challenging.

Grade history

↗

average GPA
letter grades

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

Graduate/professional standing

COMPSCI/ECE 761 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Graduate/professional standing

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

No current instructor is listed for this term. Historical reviews for Grigoris Chrysos and Ramya Vinayak show polarized experiences, with one citing clear lectures and another describing poor teaching and unhelpful homework. Ramya Vinayak also received praise for rigorous explanations of complex theoretical concepts.

GRIGORIS CHRYSOS is recorded teaching in Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

RAMYA KORLAKAI VINAYAK is recorded teaching in Spring 2023, Spring 2024, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

ROBERT NOWAK is recorded teaching in Spring 2016, Fall 2017, Fall 2018, Spring 2020, Spring 2022, Spring 2023. 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 2026

Schedule 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

Reviews for historical instructors Jerry Zhu, Grigoris Chrysos, and Ramya Vinayak praise clear, intuitive explanations of complex machine learning concepts. Conversely, reviews for Kangwook Lee and Ramya Vinayak describe polarized experiences ranging from highly committed teaching to derisive behavior.

Recent recorded grades — Spring 2024: 3.74 GPA, 87.0% A/AB (n=23 letter grades); Spring 2025: 3.54 GPA, 69.2% A/AB (n=39 letter grades); Spring 2026: 4.00 GPA, 100.0% A/AB (n=8 letter grades).

difficulty & workload

Historical reviews of Kangwook Lee, Ramya Vinayak: Student experiences with Kangwook Lee and Ramya Vinayak are polarized. Some praise their commitment and knowledge, while others describe derisive behavior, insufficient prerequisites, and a frustrating classroom environment.

Topics

Probabilistic, algebraic, and geometric models
Learning algorithms and optimization methods
Applications of machine learning

Skills

Mathematical foundations of machine learning theory
Mathematical analysis of learning algorithms and optimization
Applications of machine learning

Grades

Latest available · Spring 2026— not enough history to project Fall 2026.

Grade distribution · % of letter grades

Grades over time

Through Spring 2026

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.

Download part 1

Selected offering source records

No offering records for the selected term.

Raw records

[]

Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

Download part 1

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades

Model outputs & technical records

nvidia/Qwen3.6-35B-A3B-NVFP4

LLM outputs across runs

Download part 1

Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1

Model & dataset provenance

{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "58ef820e34fe4f293f289f37438409809b5fab3838ebcd02c66751151d671e07",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}