Recent recorded grades — Spring 2025: 3.12 GPA, 52.9% A/AB (n=17 letter grades).
Spring 2027
Linear Algebra and Optimization
Linear Algebra and Optimization introduces linear algebra, multivariable calculus, and optimization theory with applications to data science, implemented in Python.
offering recorded4 credits
Recorded instructors · Spring 2027 Sebastien Roch3.1/5
Summary
1 / 3
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).
What you’ll be able to do
Recall and state the formal definitions, properties, and theorems associated to elementary linear algebra (e.g., matrix, eigenvector, rank, linear independence, vector space, etc.).
Verify if a mathematical object has a given property used in elementary linear algebra (e.g., that a matrix is invertible, that a set is a vector subspace, that a vector is an eigenvector, etc.).
Check the premises of theorems used in elementary linear algebra in order to apply their conclusions (e.g., that a given matrix has zero determinant and therefore cannot be inverted).
Resolve algebraic statements related to elementary linear algebra through appropriate computations, including using classical algorithms to solve data science problems using linear algebra and their implementation in Python.
Analyze the behavior of functions of several variables, including their asymptotic behavior, local behavior, and existence of extrema with the tools of calculus (e.g., limits and continuity of multidimensional functions, derivatives and integrals of vector valued functions, arc length and curvature, partial derivatives, tangent planes, etc.).
Express informal mathematical arguments in English using appropriate mathematical terminology and notation.
Source: UW Guide
Grade history
↗ average GPA
4343 letter grades
A25.6%25.6%
AB18.6%18.6%
B25.6%25.6%
BC11.6%11.6%
C11.6%11.6%
D7.0%7.0%
F0.0%0.0%
All recorded terms · compare terms & instructors
Prerequisites
Course mapMATH 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
- All of
- MATH 222
- Any of
- Any of
- placement inCOMP SCI 300
- Not eligible with
Professors
Spring 2027Historical 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
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 0 / 90 | 0 |
| DIS 301 | Classroom Instruction | 0 / 30 | 0 |
| DIS 302 | Classroom Instruction | 0 / 30 | 0 |
| DIS 303 | Classroom Instruction | 0 / 30 | 0 |
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
- Linear algebra fundamentals
- Multivariable calculus
- Optimization methods
- Data science applications
Skills
- Elementary linear algebra definitions and theorems
- Verifying linear algebra properties
- Computing solutions to linear algebra problems in Python
- Multivariable calculus analysis
- Optimization techniques for data science
Grades
Latest available · Spring 2026— not enough history to project Spring 2027.
average GPA
A / AB grades
letter grades
Instructor
Grade distribution · % of letter grades
A19.2%19.2%
AB19.2%19.2%
B30.8%30.8%
BC7.7%7.7%
C19.2%19.2%
D3.8%3.8%
F0.0%0.0%
Grades over time
Through Spring 2026
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.
Selected offering source records
Linear Algebra and Optimization
Recorded 2026-10-14Raw records
[
{
"run_id": "20261014T203208-0851c9a1",
"semester": "1274",
"observed_at": "2026-10-14 17:50:09.728220+00:00",
"offering_id": "1274:600:026960",
"course_id": "MATH 345",
"course_uid": "course_2e0053b739243eda32fb0fc2",
"term_id": "1274",
"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 gradesDataset & model
View the dataset on Hugging Face Recorded 2026-10-14
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "5a454f099aea4ea0a46c3c1c63d9ce0c7e2195ba3321057c3566a46f6cbe5c35",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}