Spring 2027

Introduction to Computer Vision

Introduction to Computer Vision covers image formation, feature detection, motion estimation, 3D reconstruction, and object recognition with project-based MATLAB work.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

Recent recorded grades — Fall 2025: 3.50 GPA, 77.3% A/AB (n=88 letter grades).

What you’ll be able to do

  • Describe a broad range of fundamental concepts in 2D and 3D computer vision, including basic image processing, 2D image recognition, 3D sensing and motion recovery

  • Explain basic theories, current approaches, key concepts, and common practices in computer vision.

  • Design basic statistics and machine learning algorithms for computer vision problems, conduct experiments to evaluate the proposed approaches, and analyze and interpret the results.

  • Develop basic computer vision applications and projects in MATLAB.

  • Prepare and deliver clear and concise oral presentations.

Source: UW Guide

Grade history

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average GPA
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All recorded terms · compare terms & instructors

Prerequisites

Course map

COMP SCI 400 and (MATH 320, 340, 341, 345 or 375) and (STAT 311, 324, 333, 340, 371,STAT/​MATH 309, 431,MATH 331 or 531) or graduate/professional standing

COMPSCI 566

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

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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Meeting source records
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Student experience

the class

Recent recorded grades — Fall 2025: 3.50 GPA, 77.3% A/AB (n=88 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object recognition
  • Applications including 3D mapping, virtual characters, photo/video databases, HCI, video surveillance, and vehicle navigation
  • Computer vision and machine learning techniques, sensing, and imaging technologies

Skills

  • Designing statistics and machine learning algorithms for computer vision problems
  • Developing basic computer vision applications and projects in MATLAB
  • Preparing and delivering clear and concise oral presentations

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
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BC
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More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1316 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

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Raw records 
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Student reviews

Original comments behind the course and instructor summaries.

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Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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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": "2fc011ceb7cb5315b3ff716bdbed8417c80fa50f38affd13c1248fbb0c974764",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}