A&O SCI C111
Introduction to Machine Learning for Physical Sciences
Grades
3.63
Average grade awarded in A&O SCI C111. Not a student rating: this is what the grades were.
In the A range
80%
of the 65 letter grades
D, F or W
4.6%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
2%
1 of 1 student passed
The registrar's own records: 65 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 3.60 to 3.67.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 13 · 20.0%
- A 33 · 50.8%
- A- 6 · 9.2%
- B+ 3 · 4.6%
- B 4 · 6.2%
- B- 1 · 1.5%
- C 2 · 3.1%
- D+ 1 · 1.5%
- F 2 · 3.1%
- P 1 of 67
- I 1 of 67
Percentages are of the 65 letter grades. Grey bars are non-letter outcomes: passes (P) and 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 A 2026 |
|---|---|---|---|---|---|---|---|
| LOZINSKI, ALEXANDER RICHARD reviews ↗ | 3.64 | 3.64 | 55 | 2 | 82% | - | not teaching it |
| BORTNIK, JACOB reviews ↗ | 3.62 | 3.60 | 10 · | 1 | 70% | - | 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 A&O SCI C111 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, 90 minutes; laboratory, 90 minutes. Designed for physical sciences students. Practical, hands-on introduction to seven of most popular algorithms of machine learning (ML). Students gain most practical skills to start working in industry or research immediately, using popular Python programming language, together with SciKitLearn ML library, and covering essential theory to understand what algorithms do. Focus on solving typical problems that arise in physical sciences. Covers algorithms in broad areas of ML, including supervised learning (regression and classification) and unsupervised learning (clustering and dimensionality reduction). Lectures and programming exercises. Concurrently scheduled with course C204. P/NP or letter grading.
Sections in Summer Session A 2026
From the people who took it
Student reviews
What taking A&O SCI C111 was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
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