Fall 2026

Introduction to Algorithms

Introduction to efficient algorithm design paradigms and analysis of computational intractability.

offering recorded4 credits
Recorded instructors · Fall 2026 Sandeep Silwal3.4/5Marc Renault2.5/5

Summary

1 / 7

The course is consistently described as hard, requiring significant extra time outside of class. Workload includes challenging homeworks and a stressful final exam that varies in difficulty, with grading often relying on participation or bonus points.

Grade history

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average GPA
letter grades
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AB
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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 577 used by

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

Prerequisite text tree

Professors

Fall 2026
/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 2.78/5 from 9 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.

Sandeep Silwal receives praise for engaging lectures where he explains concepts on the chalkboard rather than reading slides. Reviewers describe him as passionate, helpful, and caring, with some preferring his instruction over Marc Renault.

Other reviewers criticize Silwal for being rude, unresponsive to student input, and poor at time management. They report that course materials are often unclear and that lectures may not align well with exam content.

Recent recorded grades — Fall 2024: 2.65 GPA, 39.4% A/AB (n=33 letter grades); Fall 2025: 3.13 GPA, 43.0% A/AB (n=86 letter grades). Includes jointly taught sections.

Marc Renault

/5Adjusted rating
/5RMP 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.

Marc Renault is described as kind and reachable, with clear expectations and well-structured course materials. Reviewers note he explains topics well, though the class content itself remains difficult.

Many reviewers criticize his lectures as rushed, poorly explained, or disconnected from assignments. Some report rude behavior, lack of student support, and exams focused on difficulty rather than learning.

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

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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction287 / 00
DIS 311Classroom Instruction23 / 00
DIS 312Classroom Instruction53 / 00
DIS 313Classroom Instruction52 / 00
DIS 314Classroom Instruction57 / 00
DIS 315Classroom Instruction52 / 00
DIS 316Classroom Instruction50 / 00
LEC 002Classroom Instruction141 / 1500
DIS 321Classroom Instruction48 / 500
DIS 322Classroom Instruction48 / 500
DIS 323Classroom Instruction45 / 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

Sandeep Silwal is praised for engaging lectures and clear explanations, though the course remains difficult. Marc Renault receives mixed reviews, with some finding him kind and structured, while others criticize his rushed lectures and poor teaching style.

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

The course is consistently described as hard, requiring significant extra time outside of class. Workload includes challenging homeworks and a stressful final exam that varies in difficulty, with grading often relying on participation or bonus points.

Marc Renault's lectures are criticized for being rushed and reading off slides, forcing reliance on external resources. Silwal's board work and online responsiveness are valued, though some find his explanations confusing or his time management poor.

Topics

  • Algorithm design paradigms: greedy, divide-and-conquer, dynamic programming, reductions, randomness.
  • Computational intractability and NP-complete problems.

Skills

  • Algorithm design and analysis using greedy, divide-and-conquer, and dynamic programming paradigms.
  • Understanding computational intractability and handling NP-complete problems.

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 · 0.09 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 577 · Fall 2026

Introduction to Algorithms

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": "1272:266:004289",
    "course_id": "COMPSCI 577",
    "course_uid": "course_de12e29109d0c90ee086fcec",
    "term_id": "1272",
    "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
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-09-07

Download the original dataset
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": "b9b5e02d1c99fd9de837b4d25bca82a611c8cc912216c82f095b31bbfcee8f02",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}