Fall 2026

Mathematical Methods in Data Science

MATH 535 teaches mathematical methods for data science, including matrix factorizations, optimization, and probabilistic models.

offering recorded3 credits
Recorded instructors · Fall 2026 Thu Le3.2/5

Summary

1 / 6

Historical reviews of Hanbaek Lyu, Sebastien Roch: Reviewers describe the homework and exams as challenging, with some noting that the course feels like a proof-heavy extension of linear algebra rather than a data science course.

Grade history

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

Prerequisites

Course map

(MATH 320, 340, 341, 345, or 375) and (STAT/​MATH 309, 431,MATH 531, or STAT 311) and (MATH 322, 341, 375, 421, or 467, or COMP SCI 577), graduate/professional standing, or declared in Mathematics VISP (graduate or undergraduate)

MATH 535

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

Prerequisite text tree

Professors

Fall 2026

Thu Le

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.63/5 from 19 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

No course-specific feedback yet.

Historical instructors & teaching patterns

Historical reviews of Sebastien Roch: Sebastien Roch teaches MATH 535 with mixed results. Some students find his lectures confusing and rigorous, requiring self-study, while others praise his deep understanding and helpfulness. Reviewers disagree on exam difficulty, with some finding them easy and others challenging.

HANBAEK LYU is recorded teaching in Spring 2023. Recorded history may be incomplete and does not establish a future schedule.

SEBASTIEN ROCH is recorded teaching in Spring 2020, Fall 2020, Spring 2022, Fall 2022, Spring 2024, Spring 2025. 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.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction47 / 5010

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

Historical reviews for MATH 535 are polarized, with students describing instructors as either confusing and rigorous or helpful and accessible. The course material relies heavily on self-study and specific lecture notes provided by the teaching staff.

Recent recorded grades — Spring 2025: 3.44 GPA, 63.8% A/AB (n=47 letter grades); Fall 2025: 3.46 GPA, 76.5% A/AB (n=34 letter grades); Spring 2026: 3.87 GPA, 91.3% A/AB (n=46 letter grades).

difficulty & workload

Historical reviews of Hanbaek Lyu, Sebastien Roch: Reviewers describe the homework and exams as challenging, with some noting that the course feels like a proof-heavy extension of linear algebra rather than a data science course.

Historical reviews of Hanbaek Lyu, Sebastien Roch: Students report that instructors are generally accessible and willing to help, with lecture notes and an online textbook serving as key resources for self-learning and exam preparation.

Topics

  • Matrix factorizations
  • Optimization theory and algorithms
  • Probabilistic models
  • Finite Markov chains

Skills

  • Matrix factorization and optimization
  • Probabilistic modeling and Markov chains
  • Applied mathematical problem solving

Grades

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

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

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

1283 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.

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Selected offering source records
MATH 535 · Fall 2026

Mathematical Methods in Data Science

Recorded 2026-09-07
Raw records 
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:600:025408",
    "course_id": "MATH 535",
    "course_uid": "course_85a2851be919e3db46f0fbc1",
    "term_id": "1272",
    "source_course_id": "025408",
    "source_subject_id": "600",
    "title": "Mathematical Methods in Data Science",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
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.

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Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "0f9c08ec751d4f6d7fa09e2bd93af674864dcd94db61398bf3a36cf849936f1a",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}