BIOENGR 175
Machine Learning and Data-Driven Modeling in Bioengineering
Grades
3.54
Average grade awarded in BIOENGR 175. Not a student rating: this is what the grades were.
In the A range
61%
of the 295 letter grades
D, F or W
1.7%
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: 295 grades over 4 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 4 terms ran 3.42 to 3.78.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 18 · 6.1%
- A 105 · 35.6%
- A- 58 · 19.7%
- B+ 53 · 18.0%
- B 38 · 12.9%
- B- 13 · 4.4%
- C+ 1 · 0.3%
- C 5 · 1.7%
- C- 1 · 0.3%
- F 3 · 1.0%
- I 6 of 303
- DR 2 of 303
Percentages are of the 295 letter grades. Grey bars are non-letter outcomes: incompletes (I) and withdrawals (DR).
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.
Withdrawals are the exception to "excluded": DR is not scored, but it IS counted in the D, F or W figure above, which is reported over the letter grades plus the withdrawals. Pass/no pass and S/U outcomes are outside that figure on both sides.
By instructor
| Instructor | Predicted GPA | Raw | n | Terms | A range | Workload | Summer Session C 2026 |
|---|---|---|---|---|---|---|---|
| MEYER, AARON S reviews ↗ | 3.54 | 3.54 | 295 | 4 | 61% | - | 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 BIOENGR 175 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
(Formerly numbered C175.) Lecture, four hours; laboratory, two hours; outside study, six hours. Requisites: Civil Engineering M20 or Mechanical and Aerospace Engineering M20 or Computer Science 31, Mathematics 32B, 33A. Overview of foundational data analysis and machine-learning methods in bioengineering, focusing on how these techniques can be applied to interpret experimental observations. Topics include probabilities, distributions, cross-validation, analysis of variance, reproducible computational workflows, dimensionality reduction, regression, hidden Markov models, and clustering. Students gain theoretical and practical knowledge of data analysis and machine-learning methods relevant to bioengineering. Application of these methods to experimental data from bioengineering studies. Students become sufficiently familiar with these techniques to design studies incorporating such analyses, execute analysis, and work in teams using similar approaches, and ensure correctness of their results. Letter grading.
Sections in Summer Session C 2026
From the people who took it
Student reviews
What taking BIOENGR 175 was like: the work, the teaching, and what students wish they’d known.
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