Spring 2027

Numerical Analysis

Advanced numerical analysis covering polynomial interpolation, approximation, differentiation, integration, splines, and ODE solvers.

offering recorded3 credits
Recorded instructors · Spring 2027 Thu Le3.2/5

Summary

1 / 6

Past student reviews · Chris Rycroft, Laurel Ohm

One student found the homework tedious but manageable, while another noted a heavy workload that was doable with time. Rycroft's exams were challenging but included partial credit, whereas Ohm's exams were described as having very reasonable difficulty.

What you’ll be able to do

  • Recall and state the formal definitions of the mathematical objects and their properties used in numerical analysis (e.g., Lagrange polynomials, Gibbs phenomenon, Runge phenomenon, orthogonal polynomials, recurrence relation, Gaussian quadrature points, splines, etc.).

  • Use different techniques of numerical analysis in their appropriate settings (e.g., polynomial interpolation, least square approximation, discrete Fourier transform, the Golub-Welsch algorithm, fast Fourier transform, trapezoidal rule and Simpson's rule, numerical differentiation, forward and backward Euler's method, etc.).

  • State the main theoretical results related to the error analysis for different methods (e.g., least square error, numerical integration using a Riemann sum, the trapezoidal rule, Simpson's rule and Gaussian quadratures, (semi-)discrete Fourier transform, forward and backward Euler, etc.), and recall the arguments for these theorems and the underlying logic of their proofs.

  • 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
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All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 320, 340, 341, 345, or 375), (MATH 322, 341, 375, 421, or 521), and (COMP SCI 200, 220, 300, or placement into COMP SCI 300), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)

COMPSCI/MATH 514

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

Prerequisite text tree

Professors

Spring 2027

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

CHENXI WU is recorded teaching in Fall 2020. Recorded history may be incomplete and does not establish a future schedule.

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

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

QIN LI is recorded teaching in Fall 2015, Fall 2016, Fall 2018, Fall 2020, Spring 2024. Recorded history may be incomplete and does not establish a future schedule.

SAVERIO SPAGNOLIE is recorded teaching in Fall 2017, Fall 2019, Fall 2021, Fall 2022, 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 / 550
LEC 001Classroom Instruction0 / 550

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 · Qin Li, Laurel Ohm

Past students described the course as tough but manageable with clear lectures and helpful instructors. Reviews highlighted that professors like Qin Li and Laurel Ohm made complex material digestible and provided accessible support.

Recent recorded grades — Spring 2025: 3.40 GPA, 66.7% A/AB (n=30 letter grades); Fall 2025: 3.57 GPA, 71.7% A/AB (n=46 letter grades); Spring 2026: 3.77 GPA, 95.2% A/AB (n=42 letter grades).

difficulty & workload

Past student reviews · Chris Rycroft, Laurel Ohm

One student found the homework tedious but manageable, while another noted a heavy workload that was doable with time. Rycroft's exams were challenging but included partial credit, whereas Ohm's exams were described as having very reasonable difficulty.

Past student reviews · Qin Li, Laurel Ohm

Students appreciated the accessibility of past instructors, noting that Qin Li was kind and approachable while Ohm posted clear handwritten notes. These resources helped students navigate the difficult content effectively.

Topics

  • Polynomial interpolation and divided differences
  • Polynomial approximation using Chebyshev and orthogonal polynomials
  • Numerical differentiation and integration
  • Splines, B-splines, and spline approximation
  • Numerical methods for ordinary differential equations

Skills

  • Polynomial interpolation and approximation techniques including Chebyshev polynomials and least-squares methods.
  • Numerical differentiation, integration, and spline approximation methods.
  • Solving initial and boundary value problems for ordinary differential equations using numerical methods.
  • Error analysis for numerical methods including quadrature rules and finite difference schemes.

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 514 · Spring 2027

Numerical Analysis

Recorded 2026-10-14
COMPSCI/MATH 514 · Spring 2027

Numerical Analysis

Recorded 2026-10-14
Raw records 
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    "title": "Numerical Analysis",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall"
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    "course_id": "COMPSCI/MATH 514",
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    "source_subject_id": "600",
    "title": "Numerical Analysis",
    "credits_min": 3,
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    "typically_offered": "Fall"
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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": "d106a40f32ec71cc4fd5147c7df168a8f6da7d0217721582d553ce29f873dbf3",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}