Fall 2026

Introduction to Artificial Intelligence

Introduction to artificial intelligence covering search, logic, machine learning, and probabilistic reasoning.

offering recorded3 credits
Recorded instructors · Fall 2026 Kirthevasan Kandasamy3.7/5Young Wu2.9/5Misha Khodak

Summary

1 / 6

Exams are described as impossible or very difficult with low averages, while homework is considered useful and constitutes the majority of the grade.

Grade history

↗
average GPA
letter grades
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AB
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All recorded terms · compare terms & instructors

Prerequisites

Course map

(COMP SCI 300 or 320), (MATH 320, 340, 341, 345, 375 or M E/​COMP SCI/​E C E 532), and (STAT 311, 324, 333, 340, 371,MATH/​STAT 309, 431,MATH 331, 531, orE C E 331), or grad/professional standing, or declared in Capstone Cert in Computer Science for Prof

COMPSCI 540

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

Prerequisite text tree

Professors

Fall 2026
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 5.00/5 from 1 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.

Recent recorded grades — Fall 2022: 3.44 GPA, 65.5% A/AB (n=165 letter grades). Includes jointly taught sections.

Young Wu

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.60/5 from 48 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.

For this course: 2.5/5 raw quality · 3.7/5 difficulty · 29 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Reviewers praise Young Wu for being responsive, patient, and passionate, with helpful office hours and engaging demos. He provides extra review sessions and fosters a supportive environment.

Wu faces criticism for unclear lectures, disorganized slides, and difficult exams misaligned with class material. Students report struggling to follow explanations and finding exams nearly impossible, suggesting independent study is necessary.

Recent recorded grades — Fall 2024: 3.40 GPA, 65.4% A/AB (n=341 letter grades); Spring 2026: 3.39 GPA, 61.5% A/AB (n=135 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Historical reviews of Jerry Zhu, Yudong Chen: Yudong Chen is a passionate lecturer with easy, interesting weekly projects, though tests are considered the hardest part of the class. Jerry Zhu's review recommends waiting for Fred Sala's section, praising Sala's ability to break down complex theories clearly, though one reviewer suggests this perspective may reflect top student views.

CHARLES ROBERT DYER is recorded teaching in Spring 2007, Fall 2007, Fall 2008, Fall 2009, Fall 2010, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Fall 2019. Recorded history may be incomplete and does not establish a future schedule.

COLLIN ENGSTROM is recorded teaching in Spring 2016, Fall 2017. Recorded history may be incomplete and does not establish a future schedule.

FRED SALA is recorded teaching in Fall 2022, Spring 2024, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

GARY DAHL is recorded teaching in Fall 2015. Recorded history may be incomplete and does not establish a future schedule.

JOSIAH HANNA is recorded teaching in Fall 2021, Spring 2023. Recorded history may be incomplete and does not establish a future schedule.

JUDE SHAVLIK is recorded teaching in Spring 2008, Fall 2011, Spring 2013, Fall 2014, Fall 2015, Fall 2016. Recorded history may be incomplete and does not establish a future schedule.

KIRTHEVASAN KANDASAMY is recorded teaching in Fall 2022. Recorded history may be incomplete and does not establish a future schedule.

MICHAEL COEN is recorded teaching in Spring 2009, Fall 2015. Recorded history may be incomplete and does not establish a future schedule.

SCOTT ALFELD is recorded teaching in Spring 2015. Recorded history may be incomplete and does not establish a future schedule.

VIKAS SINGH is recorded teaching in Spring 2010, Spring 2011. Recorded history may be incomplete and does not establish a future schedule.

YINGYU LIANG is recorded teaching in Spring 2018, Spring 2019, Fall 2020, Fall 2021, Fall 2022, Fall 2023. Recorded history may be incomplete and does not establish a future schedule.

YOUNG WU is recorded teaching in Fall 2024, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

YUDONG CHEN is recorded teaching in Fall 2021, Fall 2023, Fall 2024. 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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction128 / 1300
LEC 002Classroom Instruction127 / 1400
LEC 010Classroom Instruction40 / 500

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

Reviews for Young Wu are polarized; students praise his responsiveness and helpfulness but criticize his lectures as disorganized and exams as excessively difficult.

Recent recorded grades — Spring 2025: 3.54 GPA, 71.4% A/AB (n=555 letter grades); Fall 2025: 3.64 GPA, 82.2% A/AB (n=544 letter grades); Spring 2026: 3.46 GPA, 67.0% A/AB (n=491 letter grades).

difficulty & workload

Exams are described as impossible or very difficult with low averages, while homework is considered useful and constitutes the majority of the grade.

Lectures are often hard to follow or lack detail, requiring students to rely on extra review sessions, recordings, or self-study to understand the material.

Topics

  • Search techniques, automatic deduction, and predicate logic.
  • AI applications in problem solving, data mining, and robotics.

Skills

  • Knowledge-based search, automatic deduction, and knowledge representation.
  • Applying AI techniques to problem solving, data mining, and perception tasks.

Grades

Historical instructor

Fall 2026 · 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 5 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.

8 earlier forecasts · 0.17 GPA average error.

Grades over time

Through Fall 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

1283 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 540 · Fall 2026

Introduction to Artificial Intelligence

Recorded 2026-09-07
Raw records 
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:266:004280",
    "course_id": "COMPSCI 540",
    "course_uid": "course_e0f984e8747ee9ccaba16d75",
    "term_id": "1272",
    "source_course_id": "004280",
    "source_subject_id": "266",
    "title": "Introduction to Artificial Intelligence",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring, Summer"
  }
]
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 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
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": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "c756f9ffb501bef9c96876dfa542cee7ec16c19968455036520a87ded6428d12",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}