COM SCI M146
Introduction to Machine Learning
Lecture and discussion.
Winter 2027: 2 lectures and 6 discussions. Nothing here is open. The oldest of these readings was about 1 month ago.
Also listed as EC ENGR M146 (tentative): the same class, and each department holds its own seats and its own grade history.
Grades
3.47
Average grade awarded in COM SCI M146. Not a student rating: this is what the grades were.
In the A range
59%
of the 1,980 letter grades
D, F or W
1.4%
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: 1,980 grades over 10 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 10 terms ran 3.32 to 3.71.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 299 · 15.1%
- A 478 · 24.1%
- A- 396 · 20.0%
- B+ 346 · 17.5%
- B 169 · 8.5%
- B- 114 · 5.8%
- C+ 116 · 5.9%
- C 23 · 1.2%
- C- 11 · 0.6%
- D+ 7 · 0.4%
- D 6 · 0.3%
- D- 1 · 0.1%
- F 14 · 0.7%
- I 6 of 1,986
Percentages are of the 1,980 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 | Winter 2027 |
|---|---|---|---|---|---|---|---|
| SANKARARAMAN, SRIRAM reviews ↗ | 3.43 | 3.42 | 1,067 | 4 | 54% | average | not teaching it |
| CHANG, KAI-WEI reviews ↗ | 3.47 | 3.47 | 589 | 3 | 63% | light | not teaching it |
| GROVER, ADITYA reviews ↗ | 3.63 | 3.64 | 311 | 3 | 71% | average | not teaching it |
| DIGGAVI, SUHAS N reviews ↗ | 3.43 | 3.38 | 13 · | 1 | 62% | - | 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
The chain dibs can see is 6 courses long before COM SCI M146. Read from Fall 2026, because dibs has not read a class page for COM SCI M146 in Winter 2027 and a department can change what it requires. This is a floor, not a deadline: dibs has not read a class page for EC ENGR 10, MATH 170A, and MATH 3B, so the real chain can only be longer than this, never shorter.
Counted in courses rather than quarters: dibs does not know which of them a department lets you take together.
In Winters
Ran in 4 of the last 4 Winters.
From the terms dibs holds, which start in Fall 2021 and exclude summer. A department can run a course in a quarter it never has before.
From the registrar's catalog
(Same as Electrical and Computer Engineering M146.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 32 or Program in Computing 10C; Civil and Environmental Engineering 110 or Electrical and Computer Engineering 131A or Mathematics 170A or 170E or Statistics 100A; Mathematics 33A. Introduction to breadth of data science. Foundations for modeling data sources, principles of operation of common tools for data analysis, and application of tools and models to data gathering and analysis. Topics include statistical foundations, regression, classification, kernel methods, clustering, expectation maximization, principal component analysis, decision theory, reinforcement learning and deep learning. Letter grading.
Sections in Winter 2027
You enroll in the discussion, not the lecture, so a discussion's status is the one that decides whether you get in. A lecture can read open while every discussion under it is full.
Seat counts as of : the oldest of these 8 readings. Seat readings are snapshots, not live availability. Their timestamps show when dibs last checked.
-
Lec 1 Time TBANot published yet
Not yet assigned
3.47predicted, all instructors · 1,980 gradesno enrollment count published no history
not checked yet3 discussions. The registrar has not published seat counts for them yet.
Every discussion, with its time and seats (3)
-
Lec 2 Time TBANot published yet
Not yet assigned
3.47predicted, all instructors · 1,980 gradesno enrollment count published no history
not checked yet3 discussions. The registrar has not published seat counts for them yet.
Every discussion, with its time and seats (3)
Final enrollment, averaged across each term's sections: between 10 and 311 a term over 10 terms, summer excluded, most recently 174 in Fall 2025. That is the shape across years, not this term, which is above.
Seats over time in Winter 2027
Read once so far, so there is no line to draw yet.
What turns this into a chart
A second reading is what turns this into a chart: courses come round within about three hours, and a lecture somebody is watching goes to the front of the pass. Watching is on the lecture's own page, not this one.
How readings and alerts work
A reading and an hourly record are different things, and most charts here hold more of the second missing than present. dibs has taken readings of a section since long before it began keeping an hour-by-hour log of when it looked, so the empty stretches are dibs saying it was not keeping that log yet, not that it looked and found nothing. That is the age of the record and not a fault.
Seat counts show the last successful read, not live availability. Refreshes can be delayed by request limits or upstream interruptions. Watched sections receive priority, but an hourly update for every section is not guaranteed.
The registrar can change a seat count between our checks. Alerts depend on dibs detecting an opening and completing delivery; a quiet watch does not prove that no seat opened. Check the registrar before enrolling.
From the people who took it
Student reviews
What taking COM SCI M146 was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
No student reviews are available to read on dibs for this course yet. Took it? Tell the next person what helped, what was hard, and how you spent your time.
Looking for more perspectives?
External reviews are separate and do not count toward the dibs rating.