Fall 2026

Algorithmic Thinking

COMPSCI 477 teaches algorithmic thinking and design paradigms for solving computational problems.

no offering record for this termCredits unavailable

Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 3

Historical reviews of Anya Samak, Lydia Ashton, Lydia Zepeda: Workload includes random quizzes, group homework, and exams. One review notes exams are easy, while another finds them unlike practice questions. Group work is a consistent component.

Grade history

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average GPA
letter grades

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Prerequisites

Course map

Declared in the Capstone Certificate in Computer Sciences Kickstart Program for Professionals

Declared in the Capstone Certificate in Computer Sciences Kickstart Program for Professionals

COMPSCI 477

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Prerequisite text tree

Declared in the Capstone Certificate in Computer Sciences Kickstart Program for Professionals

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

No current instructor is listed for this term. Historical reviews for Anya Samak, Lydia Zepeda, and Lydia Ashton show varied experiences. Samak was easy with simple exams but relied on slides. Zepeda was unapproachable with confusing explanations and pop quizzes. Ashton was caring and offered extra credit but had weak lecturing skills and mandatory group work.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Fall 2026

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Section	Mode	Enrolled / capacity	Waitlist

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Student experience

the class

Historical reviews describe COMPSCI 477 as varying from easy to confusing, with frequent quizzes and group work. Students report mixed experiences regarding lecture quality and exam preparation.

difficulty & workload

Historical reviews of Lydia Ashton, Lydia Zepeda: Students find group work annoying if paired with poor partners. Lecture styles vary from reading slides to being caring but unclear. Extra credit opportunities are mentioned positively.

Topics

Divide and conquer
Dynamic programming
Greedy algorithms
Discrete mathematics
Data structures

Skills

Algorithmic problem solving
Algorithm design paradigms
Program development and implementation

Grades

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Sources & history

UW–Madison

Catalog & offerings

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Catalog observation history

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Raw records

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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Model outputs & technical records

nvidia/Qwen3.6-35B-A3B-NVFP4

LLM outputs across runs

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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": "8071ae302290571341c57d641ed775f84dbbb1dd4b0847d980db5f2ba85df7fe",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}