Fall 2026

Linear Algebra and Optimization

MATH 345 introduces linear algebra, multivariable calculus, and optimization theory with data science applications, implemented in Python.

offering recorded4 credits
Recorded instructors · Fall 2026 Sebastien Roch3.2/5Jingyi LiKevin Dao

Summary

1 / 4

Recent recorded grades — Spring 2025: 3.12 GPA, 52.9% A/AB (n=17 letter grades); Spring 2026: 2.98 GPA, 38.5% A/AB (n=26 letter grades).

Grade history

average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 222 and (COMP SCI 200, 220, 300, 310, 320, or placement inCOMP SCI 300). Not open to students with credit forMATH 320, 340, 341, or 375.

“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

Fall 2026
/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 2.33/5 from 12 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.

Recent recorded grades — Spring 2025: 3.12 GPA, 52.9% A/AB (n=17 letter grades).

No course-specific feedback yet.

No course-specific feedback yet.

Historical instructors & teaching patterns

SEBASTIEN ROCH is recorded teaching in 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
DIS 301Classroom Instruction24 / 250
DIS 302Classroom Instruction24 / 250
DIS 303Classroom Instruction24 / 250
LEC 001Classroom Instruction72 / 750

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records

Student experience

the class

Recent recorded grades — Spring 2025: 3.12 GPA, 52.9% A/AB (n=17 letter grades); Spring 2026: 2.98 GPA, 38.5% A/AB (n=26 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Vectors, analytic geometry, matrices, linear functions, independence, orthogonality, and inverses.
  • Partial derivatives, gradients, Taylor approximation, gradient descent, and Lagrange multipliers.
  • Clustering, regression, and classification.

Skills

  • Python implementation of mathematical concepts.
  • Linear algebra and differential calculus in several variables.
  • Clustering, regression, and classification techniques.
  • Partial derivatives, gradients, Taylor approximation, gradient descent, and Lagrange multipliers.

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

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
MATH 345 · Fall 2026

Linear Algebra and Optimization

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": "1272:600:026960",
    "course_id": "MATH 345",
    "course_uid": "course_2e0053b739243eda32fb0fc2",
    "term_id": "1272",
    "source_course_id": "026960",
    "source_subject_id": "600",
    "title": "Linear Algebra and Optimization",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

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
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": "d29131fb168030ba50d41570fde40efcc739abb2aa654f28cb21d3c03cb3d59c",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}