Spring 2027

Matrix Methods in Machine Learning

Matrix Methods in Machine Learning covers linear algebraic foundations including SVD, regression, and regularization applied to classification, clustering, and neural networks.

offering recorded3 credits
Recorded instructors · Spring 2027 Bernie LesieutreUlugbek Kamilov

Summary

1 / 6

Past student reviews · Robert Nowak, Eduardo Arvelo

Homework assignments were challenging but graded on completion, while summer terms were described as crammed and intense.

What you’ll be able to do

  • Use matrices and vectors to formulate classification, prediction and matrix completion problems using techniques such as least squares, regularized least squares, the singular value decomposition, subspace methods, support vector machines, neural networks and kernel methods.

  • Implement machine learning techniques for classification, prediction and matrix completion problems in software, and validate their performance on datasets using cross validation.

  • Apply advanced techniques to formulate and prove optimality of various matrix based techniques in machine learning.

Source: UW Guide 1 UW Guide 2 UW Guide 3

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 234, 320, 340, 341, or 375) and (E C E 203,COMP SCI 200, 220, 300, 301, 310, 320, or placement into COMP SCI 300), graduate/professional standing, or declared in Capstone Certificate in Computer Sciences for Professionals

COMPSCI/ECE/ME 532 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

Professors

Spring 2027

Recent recorded grades — Spring 2026: 3.29 GPA, 63.2% A/AB (n=114 letter grades).

Historical instructors & teaching patterns

EDUARDO ROMERO ARVELO is recorded teaching in Fall 2021, Fall 2023, Spring 2024, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

GRIGORIS CHRYSOS is recorded teaching in Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

MATTHEW MALLOY is recorded teaching in Fall 2019, Spring 2020, Spring 2022. Recorded history may be incomplete and does not establish a future schedule.

RAMYA KORLAKAI VINAYAK is recorded teaching in Fall 2020, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

ROBERT NOWAK is recorded teaching in Fall 2014, Fall 2015, Fall 2020, Fall 2021, Fall 2022, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

ULUGBEK KAMILOV is recorded teaching in Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

YU HU is recorded teaching in Spring 2020, Fall 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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 430
LEC 002Classroom Instruction0 / 1500
LEC 001Classroom Instruction0 / 800
LEC 002Classroom Instruction0 / 1500
LEC 001Classroom Instruction0 / 430
LEC 002Classroom Instruction0 / 1500

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 · Grigoris Chrysos

The course relies on pre-recorded video lectures, often from older years, while in-class time is dedicated to completing activities.

Recent recorded grades — Spring 2025: 3.06 GPA, 36.1% A/AB (n=108 letter grades); Fall 2025: 3.43 GPA, 70.5% A/AB (n=207 letter grades); Spring 2026: 3.34 GPA, 65.8% A/AB (n=149 letter grades).

difficulty & workload

Past student reviews · Robert Nowak, Eduardo Arvelo

Homework assignments were challenging but graded on completion, while summer terms were described as crammed and intense.

Past student reviews · Eduardo Arvelo, Ramya Vinayak

Students found in-class activities complicated and often too long for the period, leading to point losses, while exams sometimes deviated from class material.

Topics

  • Linear equations, regression, regularization, SVD, and iterative algorithms.
  • Lasso, SVMs, kernel methods, clustering, dictionary learning, neural networks, and deep learning.

Skills

  • Formulate classification, prediction, and matrix completion problems using matrices, vectors, SVD, and optimization techniques.
  • Implement machine learning algorithms in software and validate performance using cross-validation.
  • Prove optimality properties of matrix-based machine learning techniques.

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

7 earlier forecasts · 0.15 GPA average error.

Grades over time

Through Spring 2027

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

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
COMPSCI/ECE/ME 532 · Spring 2027

Matrix Methods in Machine Learning

Recorded 2026-10-14
COMPSCI/ECE/ME 532 · Spring 2027

Matrix Methods in Machine Learning

Recorded 2026-10-14
COMPSCI/ECE/ME 532 · Spring 2027

Matrix Methods in Machine Learning

Recorded 2026-10-14
Raw records 
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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
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.

Download part 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "24b151b023fe06f3210d3bbcb4c9d6a6c4217f0c6aa646c22a0a3e4e6b00269c",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
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}