ENGR 1ML
Introduction to Engineering Design: Machine Learning
Grades
4.00
Average grade awarded in ENGR 1ML. Not a student rating: this is what the grades were.
In the A range
100%
of the 62 letter grades
D, F or W
0.0%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
-
the registrar has this letter grade only this term
The registrar's own records: 62 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.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 4 · 6.5%
- A 58 · 93.5%
Percentages are of the 62 letter grades. Grey bars are non-letter outcomes.
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 | Winter 2026 |
|---|---|---|---|---|---|---|---|
| SCHMIDT, JACOB J reviews ↗ | 3.99 | 4.00 | 62 | 4 | 100% | - | 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.
From the registrar's catalog
Laboratory, three hours; outside study, three hours. Students learn basics of machine learning using popular programming language Python. Lectures cover topics in Python, machine learning, and LaTeX. Laboratory assignments challenge students to explore and engage with content from each lecture. Homework assignments culminate in final project in which students develop and train neural network image classifier, built using PyTorch, that performs significantly better than random guessing. Students report on how they chose components of their neural network. Letter grading.
Sections in Winter 2026
From the people who took it
Student reviews
What taking ENGR 1ML was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
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