Spring 2027

Introduction to Algorithms

This course covers core algorithm-design paradigms and computational intractability.

offering recorded4 credits

Recorded instructors · Spring 2027 Marc Renault2.5/5

Summary

1 / 6

Past student reviews · Marc Renault

One student found the lectures had almost no correlation to the homework, creating the feeling of taking two separate classes and requiring significant extra effort to connect the material.

What you’ll be able to do

Design and analyze efficient algorithms based on the paradigms of divide-and-conquer, dynamic programming, and greed.
Construct clear, concise, and sound proofs for algorithmic correctness and complexity statements, applying logical reasoning, synthesizing definitions and theorems, and effectively communicating structured arguments.
Formulate abstractions of computational problems, and design and analyze efficient reductions between computational problems.
Describe, select and apply paradigmatic algorithms and reductions dealing with numbers, strings, graphs, and networks, including some that go beyond the standard paradigms and/or involve more intricate mathematics.
Recognize computational intractability, demonstrate NP-hardness, and articulate the repercussions.

Source: UW Guide

Grade history

↗

average GPA
letter grades

All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH/COMP SCI 240 or STAT/COMP SCI/MATH 475) and (COMP SCI 367 or 400), or graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for Professionals

- - - COMPSCI/MATH 240
    - COMPSCI/MATH/STAT 475
    take one
  - - COMP SCI 367
    - COMPSCI 400
    take one
  take all
- graduate/professional standing
- declared in the Capstone Certificate in Computer Sciences for Professionals
take one

COMPSCI 577 used by

MATH 535

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Any of
- All of
  - Any of
    - COMPSCI/MATH 240
    - COMPSCI/MATH/STAT 475
  - Any of
    - COMP SCI 367
    - COMPSCI 400
- graduate/professional standing
- declared in the Capstone Certificate in Computer Sciences for Professionals

Professors

Spring 2027

Marc Renault

Adjusted rating

RMP difficulty

captured reviews

About this rating

Raw average: 2.33/5 from 168 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: 1.8/5 raw quality · 4.5/5 difficulty · 124 reviews

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

Past student reviews · Marc Renault

Marc Renault taught a difficult course where students reported lectures were rushed, poorly explained, or disconnected from homework, while others noted clear expectations and generous grading for bonus points. Opinions on his demeanor varied sharply, with some finding him kind and reachable, while others described him as rude, unhelpful, or dismissive of student questions.

Recent recorded grades — Spring 2025: 2.97 GPA, 47.3% A/AB (n=433 letter grades); Fall 2025: 2.89 GPA, 37.7% A/AB (n=305 letter grades); Spring 2026: 2.75 GPA, 36.7% A/AB (n=368 letter grades).

Historical instructors & teaching patterns

Past student reviews · Alexander Brooks, Shuchi Chawla

Alexander Brooks thought deeply about algorithms, sometimes making material confusing, but was always willing to stop and explain. Shuchi Chawla delivered detailed lectures, and although exams were hard, a generous curve helped students. Both instructors emphasized thoroughness and support, though students noted the course required significant independent thinking and preparation.

ALEXANDER BROOKS is recorded teaching in Spring 2020. Recorded history may be incomplete and does not establish a future schedule.

CHRISTOS TZAMOS is recorded teaching in Fall 2018, Spring 2020. Recorded history may be incomplete and does not establish a future schedule.

DEBORAH JOSEPH is recorded teaching in Spring 2007, Spring 2008, Spring 2009, Fall 2009, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015. Recorded history may be incomplete and does not establish a future schedule.

ERIC BACH is recorded teaching in Fall 2008, Fall 2013, Spring 2015, Spring 2016, Fall 2016, Fall 2019, Fall 2021, Fall 2023. Recorded history may be incomplete and does not establish a future schedule.

JEREMY MCMAHAN is recorded teaching in Spring 2020. Recorded history may be incomplete and does not establish a future schedule.

JIN-YI CAI is recorded teaching in Fall 2011, Fall 2017, Fall 2020, Spring 2022. Recorded history may be incomplete and does not establish a future schedule.

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

MARC RENAULT is recorded teaching in Spring 2020, Fall 2021, Spring 2022, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

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

SHUCHI CHAWLA is recorded teaching in Fall 2006, Spring 2010, Fall 2010, Spring 2012, Fall 2012, Fall 2014, Spring 2016, Spring 2017, Spring 2018, Fall 2018, Spring 2020. 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
LEC 001	Classroom Instruction	0 / 225
DIS 311	Classroom Instruction	0 / 45
DIS 312	Classroom Instruction	0 / 45
DIS 314	Classroom Instruction	0 / 45
DIS 313	Classroom Instruction	0 / 45
LEC 002	Classroom Instruction	0 / 225
DIS 321	Classroom Instruction	0 / 45
DIS 322	Classroom Instruction	0 / 45
DIS 323	Classroom Instruction	0 / 45
DIS 324	Classroom Instruction	0 / 45
DIS 325	Classroom Instruction	0 / 45
DIS 315	Classroom Instruction	0 / 45

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 · Marc Renault

Students described the course as difficult but well-structured, noting that clear expectations and a participation-heavy grading scheme made the semester less stressful, though the final exam remained a significant challenge.

Recent recorded grades — Spring 2025: 3.06 GPA, 52.4% A/AB (n=483 letter grades); Fall 2025: 3.04 GPA, 45.2% A/AB (n=438 letter grades); Spring 2026: 2.82 GPA, 39.9% A/AB (n=501 letter grades).

difficulty & workload

Past student reviews · Marc Renault

One student found the lectures had almost no correlation to the homework, creating the feeling of taking two separate classes and requiring significant extra effort to connect the material.

Past student reviews · Marc Renault

Opinions on Marc Renault’s teaching varied widely; some students appreciated his kindness, while others criticized his rushed lectures and felt he failed to explain concepts clearly or provide useful visual aids.

Topics

Algorithm design paradigms such as greed, divide-and-conquer, dynamic programming, reductions, and randomness.
Computational intractability and NP-complete problems.

Skills

Design and analyze efficient algorithms using standard algorithmic paradigms.
Prove algorithm correctness and complexity claims.
Construct reductions between computational problems.
Recognize and demonstrate NP-hardness.

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 · 0.20 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 577 · Spring 2027

Introduction to Algorithms

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": "1274:266:004289",
    "course_id": "COMPSCI 577",
    "course_uid": "course_de12e29109d0c90ee086fcec",
    "term_id": "1274",
    "source_course_id": "004289",
    "source_subject_id": "266",
    "title": "Introduction to Algorithms",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Fall, Spring"
  }
]

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

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

Model & dataset provenance

{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "d5b79559c6278d342a51d9e8fa3a4135dd623b5fc2ae4af27ee4657192e1bf5b",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}

Introduction to Algorithms

Summary

What you’ll be able to do

Grade history

Prerequisites

Professors

Marc Renault

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