Spring 2027

Computer Vision

Computer Vision covers fundamentals of image analysis, including acquisition, geometry, enhancement, scene characteristics, segmentation, shape and texture analysis, and parallel algorithms.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Mohit Gupta

The course included seven challenging homework assignments, with the third and fourth considered the hardest, and required Matlab proficiency.

What you’ll be able to do

  • Develop basic computer vision applications using a programming environment

  • Formulate computer vision research problems motivated from real-world applications

  • Evaluate and compare existing solutions to a computer vision problem

  • Design approaches for solving computer vision problems based on a broad range of fundamental concepts in 2D and 3D computer vision, sensing and recognition

  • Communicate solutions verbally and in writing to justify choices while designing solutions

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

Graduate/professional standing

  • Graduate/professional standing
COMPSCI/ECE 766

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

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

MOHIT GUPTA is recorded teaching in Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023, 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 001Online Only0 / 600
LEC 001Online Only0 / 600

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 · Mohit Gupta

A student described the professor as kind and patient, noting that printed slides were distributed only in lecture.

Recent recorded grades — Spring 2023: 3.84 GPA, 95.2% A/AB (n=62 letter grades); Spring 2024: 3.87 GPA, 91.5% A/AB (n=94 letter grades); Spring 2026: 3.84 GPA, 94.8% A/AB (n=58 letter grades).

difficulty & workload

Past student reviews · Mohit Gupta

The course included seven challenging homework assignments, with the third and fourth considered the hardest, and required Matlab proficiency.

Past student reviews · Mohit Gupta

The reviewer found the open-ended semester project to be a nice aspect of the course.

Topics

  • Image acquisition, geometry, enhancement, and recovery of physical scene characteristics.
  • Shape-from techniques, segmentation, perceptual organization, and representation of 2D objects.
  • Shape analysis, texture analysis, goal-directed and model-based systems.
  • Parallel algorithms and special-purpose architectures.

Skills

  • Developing basic computer vision applications using a programming environment.
  • Formulating computer vision research problems motivated from real-world applications.
  • Evaluating and comparing existing solutions to computer vision problems.
  • Designing approaches for solving computer vision problems using fundamental 2D and 3D concepts.
  • Communicating solutions verbally and in writing to justify design choices.

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 4 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

5 earlier forecasts · 0.05 GPA average error.

Grades over time

Through Spring 2027

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.

Download part 1
Selected offering source records
COMPSCI/ECE 766 · Spring 2027

Computer Vision

Recorded 2026-10-14
COMPSCI/ECE 766 · Spring 2027

Computer Vision

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:266:004336",
    "course_id": "COMPSCI/ECE 766",
    "course_uid": "course_454ae4ccf39aa0d54593f0ce",
    "term_id": "1274",
    "source_course_id": "004336",
    "source_subject_id": "266",
    "title": "Computer Vision",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Occasionally"
  },
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:320:004336",
    "course_id": "COMPSCI/ECE 766",
    "course_uid": "course_454ae4ccf39aa0d54593f0ce",
    "term_id": "1274",
    "source_course_id": "004336",
    "source_subject_id": "320",
    "title": "Computer Vision",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Occasionally"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
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.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "0d7796885e0762c997f9b3eddd6bbae81e597f5cdbbda3acf30d9fca69903b06",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}