Spring 2027

Numerical Linear Algebra

Covers direct and iterative solutions for linear/nonlinear systems and eigenproblems, including LU/QR factorizations, least squares, and algorithmic stability.

offering recorded3 credits
Recorded instructors · Spring 2027 Yue Sun

Summary

1 / 6

Past student reviews · Amos Ron

One past student said Amos Ron’s homeworks were 10–15 pages biweekly. Another said the homework took many hours and lectures met three days a week for 75 minutes.

What you’ll be able to do

  • Recall and state the formal definitions of the mathematical objects and their properties used in numerical linear algebra (e.g., matrix norms, the condition number, diagonalizability, under- and over-determined linear systems, normal equations, pseudoinverse, etc.).

  • Use different algorithms of numerical linear algebra in their appropriate settings (e.g., LU factorization, QR decomposition, Gram–Schmidt process, singular value decomposition, power method, Arnoldi iteration, etc.).

  • State the main theoretical results in the field (e.g., error bounds on the numerical solution of linear systems, algorithm stability, Gershgorin circle theorem, eigenvalue sensitivity to perturbations, etc.).

  • Describe the complexity of different algorithms and how this affects the choice of algorithm for a particular purpose. Apply general principles for the efficient computer implementation of numerical linear algebra.

  • Convey arguments in oral and written forms using English and appropriate mathematical terminology, notation and grammar.

  • Identify applications of course content in current areas of research.

Source: UW Guide 1 UW Guide 2

Grade history

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

All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 320, 340, 341, 345, or 375) and (COMP SCI 200, 220, 300, 310, or placement into COMP SCI 300), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)

COMPSCI/MATH 513

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

Prerequisite text tree

Professors

Spring 2027

Recent recorded grades — Spring 2026: 3.04 GPA, 58.7% A/AB (n=63 letter grades).

Historical instructors & teaching patterns

AMOS RON is recorded teaching in Spring 2007, Spring 2009, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

YUE SUN is recorded teaching in Spring 2026. 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 / 660
LEC 001Classroom Instruction0 / 720

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 · Amos Ron

A past student found Amos Ron’s explanations intuitive and said attending every lecture saved at least three hours of textbook reading. Another found him interesting and funny.

Recent recorded grades — Spring 2024: 3.24 GPA, 57.1% A/AB (n=35 letter grades); Spring 2025: 3.15 GPA, 50.0% A/AB (n=46 letter grades); Spring 2026: 3.04 GPA, 58.7% A/AB (n=63 letter grades).

difficulty & workload

Past student reviews · Amos Ron

One past student said Amos Ron’s homeworks were 10–15 pages biweekly. Another said the homework took many hours and lectures met three days a week for 75 minutes.

Past student reviews · Amos Ron

One past student found Amos Ron’s rambling and lack of examples frustrating. Another said he would not re-explain if a student did not understand.

Topics

  • Linear and nonlinear system solving
  • Matrix factorizations and numerical analysis
  • Iterative methods and least squares
  • Eigenvalue problems

Skills

  • Define and apply core concepts such as matrix norms, condition numbers, and diagonalizability.
  • Select and implement algorithms like LU factorization, QR decomposition, SVD, and iterative eigenvalue methods.
  • Analyze algorithm stability, error bounds, and eigenvalue sensitivity to perturbations.
  • Evaluate computational complexity to guide algorithm selection and efficient implementation.

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

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

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.

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Selected offering source records
COMPSCI/MATH 513 · Spring 2027

Numerical Linear Algebra

Recorded 2026-10-14
COMPSCI/MATH 513 · Spring 2027

Numerical Linear Algebra

Recorded 2026-10-14
Raw records 
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    "title": "Numerical Linear Algebra",
    "credits_min": 3,
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    "offering_id": "1274:600:011680",
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    "title": "Numerical Linear Algebra",
    "credits_min": 3,
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    "typically_offered": "Spring"
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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.

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.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "d43ad2e7f21656481d35455a49bd751547fa35df681da8c938788fd427ca88e1",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}