Spring 2027

Advanced Algorithms

Advanced paradigms for designing and analyzing efficient algorithms, covering randomness, linear programming, semi-definite programming, and applications to NP-hard problems.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Eric Bach, Christos Tzamos

The course was hard and math-oriented, with proof-heavy homework. Prof. Bach noted the workload was average, consisting of two homework assignments and one exam.

Grade history

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average GPA
letter grades
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Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI 787

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

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

CHRISTOS TZAMOS is recorded teaching in Fall 2019, Fall 2020, Spring 2022. Recorded history may be incomplete and does not establish a future schedule.

ERIC BACH is recorded teaching in Fall 2006, Fall 2010, Spring 2012, Fall 2014, Spring 2018, Spring 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

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

Meeting source records
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Student experience

the class

Past student reviews · Christos Tzamos

Students described this advanced algorithms course as challenging but useful, with Prof. Tzamos providing great intuition for tough math concepts and covering cutting-edge research.

Recent recorded grades — Spring 2023: 3.76 GPA, 88.2% A/AB (n=17 letter grades); Fall 2024: 3.77 GPA, 97.0% A/AB (n=33 letter grades); Spring 2026: 3.95 GPA, 100.0% A/AB (n=22 letter grades).

difficulty & workload

Past student reviews · Eric Bach, Christos Tzamos

The course was hard and math-oriented, with proof-heavy homework. Prof. Bach noted the workload was average, consisting of two homework assignments and one exam.

Past student reviews · Eric Bach

Prof. Bach made the material approachable by being willing to explain topics and answer questions in class, which reduced the intimidation factor for students.

Topics

  • Randomness, linear programming, and semi-definite programming
  • Data structures, NP-hard optimization, learning, on-line and distributed problems

Skills

  • Design and analysis of efficient algorithms using randomness, linear programming, and semi-definite programming
  • Approximation of NP-hard optimization problems and handling on-line and distributed problems

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
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BC
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Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

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

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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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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
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": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "42da04399dbc467d3dba00305ea16e6618c3ac594bb39315dec63fd62efa3e50",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}