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Fall 2026
Advanced Algorithms
COMPSCI 787 covers advanced algorithm design and analysis, focusing on randomness, linear programming, and semi-definite programming for optimization and distributed problems.
Recorded instructors · Fall 2026 Manolis Vlatakis Gkaragkounis
Summary
1 / 6
The course is mathematically rigorous with heavy proof requirements, though one historical review notes an average workload of two homeworks and one exam under a different instructor.
Grade history
↗ average GPA
letter grades
letter grades
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All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Graduate/professional standing
Professors
Fall 2026Historical instructors & teaching patterns
Manolis Vlatakis Gkaragkounis is the current instructor, but reviews focus on historical faculty. Christos Tzamos provided strong intuition for math-heavy algorithms, though one student found the proof-based content overwhelming. Eric Bach made the difficult course approachable and responsive to student questions.
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
Fall 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 29 / 30 | 5 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Historical reviews describe COMPSCI 787 as a challenging but useful course covering cutting-edge research, with students valuing the deep mathematical intuition provided by past instructors.
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
The course is mathematically rigorous with heavy proof requirements, though one historical review notes an average workload of two homeworks and one exam under a different instructor.
Historical reviews of Christos Tzamos: Students report conflicting experiences with the mathematical focus; some find the proofs essential for understanding, while others dislike the lack of coding and high difficulty.
Topics
Skills
Grades
Latest available · Spring 2026— not enough history to project Fall 2026.
Grade distribution · % of letter grades
A
AB
B
BC
C
D
F
Grades over time
Through Spring 2026
More grade details Grade mix, volume & source data
Not enough comparable courses for Fall 2026 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.
Selected offering source records
Advanced Algorithms
Recorded 2026-09-07Raw records
[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:266:004344",
"course_id": "COMPSCI 787",
"course_uid": "course_1c792cd171ebabb5c5548c07",
"term_id": "1272",
"source_course_id": "004344",
"source_subject_id": "266",
"title": "Advanced Algorithms",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Occasionally"
}
]Rate My Professors
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Madgrades
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "2f01cb611bc75cd98e69a9b3ae0dfa144e3bfca8dba3db32f48b96af302505f8",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}