COM SCI 267A
Probabilistic Programming and Relational Learning
Grades
3.78
Average grade awarded in COM SCI 267A. Not a student rating: this is what the grades were.
In the A range
84%
of the 168 letter grades
D, F or W
0.0%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
0%
nobody in the record took it pass/no pass
The registrar's own records: 168 grades over 3 terms, Fall 2021 to Fall 2025. Every instructor is pooled here.
What is inside that record
No summer session is in that record.
The table below splits them, and term averages across those 3 terms ran 3.77 to 3.80.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 22 · 13.1%
- A 69 · 41.1%
- A- 50 · 29.8%
- B+ 19 · 11.3%
- B 8 · 4.8%
- I 3 of 171
Percentages are of the 168 letter grades. Grey bars are non-letter outcomes: incompletes (I).
How the non-letter outcomes are counted
They are excluded from the GPA entirely rather than scored, because counting a P as a 4.0 would be a fabrication.
By instructor
| Instructor | Predicted GPA | Raw | n | Terms | A range | Workload | Summer Session C 2026 |
|---|---|---|---|---|---|---|---|
| VAN DEN BROECK, GUY reviews ↗ | 3.78 | 3.78 | 168 | 3 | 84% | - | not teaching it |
Why Predicted is not the Raw average
“Predicted” is shrunk toward the course average, which is itself shrunk toward the department, so an instructor with a handful of students sits near the course mean rather than topping the list.
Grade records describe outcomes. For what a course is like to take, read student reviews written on dibs below or follow the separate external review link.
Workload and assignments
Work types reported in BruinWalk reviews of this course, across recorded instructors and terms. They may differ from this term's syllabus.
dibs has not read this course’s reviews, so it cannot say what kind of work it sets. That is dibs not having looked. It is not a course without papers or exams, and the reviews linked below are the answer until dibs catches up.
What comes first
dibs has not read a class page for COM SCI 267A in any term it holds, so it cannot say what comes before it.
Counted in courses rather than quarters: dibs does not know which of them a department lets you take together.
From the registrar's catalog
Lecture, four hours; discussion, two hours; outside study, six hours. Introduction to computational models of probability and statistical models of relational data. Study of relational representations such as probabilistic databases, relational graphical models, and Markov logic networks, as well as various probabilistic programming languages. Covers their syntax and semantics, probabilistic inference problems, parameter, and structure learning algorithms, and theoretical properties of representation and inference. Expressive statistical modeling, how to formalize and reason about complex statistical assumptions and encode knowledge in machine learning models. Survey of key applications in natural language processing, graph mining, computer vision, and computational biology. Letter grading.
Sections in Summer Session C 2026
From the people who took it
Student reviews
What taking COM SCI 267A was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
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