Spring 2027

Introduction to Artificial Intelligence

Introduction to Artificial Intelligence covering search, deduction, knowledge representation, machine learning, and probabilistic reasoning with applications in NLP, vision, and robotics.

offering recorded3 credits

Recorded instructors · Spring 2027 Young Wu2.9/5 Misha Khodak

Summary

1 / 6

Past student reviews · Young Wu

Exams were reported as extremely difficult, with one student stating they were impossible to prepare for using class materials alone. Another described the exams as 'sadistic' but noted the instructor was helpful when students reached out.

What you’ll be able to do

Identify, formulate and solve AI problems using appropriate computational approaches.
Apply knowledge of computer science, mathematics, and statistics to the analysis and enhancement of AI systems
Design and conduct AI experiments, and analyze and interpret data and outcomes.
Demonstrate their awareness of major theories, approaches, concepts, and current and classical research findings in AI.
Examine professional and ethical responsibility in AI practices for society.

Source: UW Guide

Grade history

↗

average GPA
letter grades

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 300
    - COMPSCI 320
    take one
  - - take one
    - COMPSCI/ECE/ME 532
    take one
  - take one
  take all
- - grad/professional standing
  - declared in Capstone Cert in Computer Science for Prof
  take one
take one

COMPSCI 540

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

Prerequisite text tree

Any of
- All of
  - Any of
    - COMPSCI 300
    - COMPSCI 320
  - Any of
    - Any of
    - COMPSCI/ECE/ME 532
  - Any of
- Any of
  - grad/professional standing
  - declared in Capstone Cert in Computer Science for Prof

Professors

Spring 2027

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.

Past student reviews · Young Wu

Young Wu held extra coding and review sessions, and students found him reachable and helpful. Some praised his interesting demos and patient office hours, while others noted his lectures were difficult to follow despite his qualifications.

Students reported that exams were extremely difficult and often felt disconnected from class material, whereas homeworks were described as useful and a significant portion of the grade. One student found the projects informative, noting that LLMs were allowed for competitions.

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.

Misha Khodak

No course-specific feedback yet.

Historical instructors & teaching patterns

Past student reviews · Yudong Chen

A student found Yudong Chen to be an okay lecturer who appeared passionate about the subject. The weekly projects were described as easy and interesting, while the tests were considered the hardest part of the entire class.

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.

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

Spring 2027

Schedule loads here as you scroll.

Section	Mode	Enrolled / capacity	Waitlist
LEC 002	Classroom Instruction	0 / 200	0
LEC 001	Classroom Instruction	0 / 150	0

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records

Download part 1

Student experience

the class

Past student reviews · Young Wu

Young Wu is described as a responsive instructor who uses interesting demos and allows LLMs for project competitions. These students recommend the class due to his accessibility and teaching approach.

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

Past student reviews · Young Wu

Some students found lectures disorganized and hard to follow, noting that slides lacked detail. Success often required attending extra review sessions or studying on your own time.

Topics

Search techniques, automatic deduction, knowledge representation, machine learning, and probabilistic reasoning.
Applications in problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.

Skills

Knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, and probabilistic reasoning.
Formulating and solving AI problems using computational approaches.
Designing and conducting AI experiments, analyzing and interpreting data and outcomes.
Applying AI to problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.

Grades

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 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 · 0.14 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 540 · Spring 2027

Introduction to Artificial Intelligence

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": "1274:266:004280",
    "course_id": "COMPSCI 540",
    "course_uid": "course_e0f984e8747ee9ccaba16d75",
    "term_id": "1274",
    "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

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 Download part 2 Download part 3 Download part 4

Model & dataset provenance

{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "235010623831049753d4d9ff30a29fc4bdd1a8bcd2925df9e5f3502810ee6944",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}

Introduction to Artificial Intelligence

Summary

What you’ll be able to do

Grade history

Prerequisites

Professors

Young Wu

Misha Khodak

Calendar & sections

Student experience

the class

difficulty & workload

Topics

Skills

Grades

Spring 2027 · Projected

Grades over time

Where this course fits relative to UW–Madison

Sources & history

Catalog & offerings

Student reviews

Grade history

Where this course fits relative to