MATH 156
Machine Learning
Lecture and discussion. The registrar has published a final exam time.
Fall 2026: 1 lecture and 1 discussion. Nothing here is open. The oldest of these readings was 3 hours ago.
Grades
3.51
Average grade awarded in MATH 156. Not a student rating: this is what the grades were.
In the A range
65%
of the 438 letter grades
D, F or W
2.1%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
2%
7 of 8 students passed
The registrar's own records: 438 grades over 11 terms, Fall 2021 to Fall 2025. Every instructor is pooled here.
What is inside that record
3 of those terms are summer sessions, and summer sessions are counted here.
The table below splits them, and term averages across those 11 terms ran 3.06 to 3.99.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 57 · 13.0%
- A 173 · 39.5%
- A- 55 · 12.6%
- B+ 47 · 10.7%
- B 41 · 9.4%
- B- 24 · 5.5%
- C+ 18 · 4.1%
- C 12 · 2.7%
- C- 2 · 0.5%
- D 1 · 0.2%
- D- 3 · 0.7%
- F 5 · 1.1%
- P 7 of 447
- NP 1 of 447
- I 1 of 447
Percentages are of the 438 letter grades. Grey bars are non-letter outcomes: passes (P), no-passes (NP), 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 | Fall 2026 |
|---|---|---|---|---|---|---|---|
| KASSAB, LARA reviews ↗ | 3.34 | 3.32 | 100 | 3 | 55% | light | not teaching it |
| JEONG, HALYUN reviews ↗ | 3.88 | 3.98 | 70 | 2 | 99% | - | not teaching it |
| KRISHNAGOPAL, SANJUKTA reviews ↗ | 3.30 | 3.23 | 43 | 1 | 37% | very heavy | not teaching it |
| FAROLFI, GIULIO reviews ↗ | 3.59 | 3.63 | 40 | 1 | 73% | - | not teaching it |
| LIAO, CHUNYANG reviews ↗ | 3.61 | 3.66 | 37 | 1 | 86% | - | not teaching it |
| MENZ, GEORG reviews ↗ | 3.42 | 3.38 | 36 | 1 | 42% | - | not teaching it |
| MONTUFAR CUARTAS, GUIDO FRANCISCO reviews ↗ | 3.32 | 3.23 | 36 | 1 | 44% | - | not teaching it |
| NEGRINI, ELISA reviews ↗ | 3.27 | 3.13 | 27 | 1 | 37% | - | not teaching it |
| NGUYEN, MINH TAN reviews ↗ | 3.72 | 3.86 | 26 | 1 | 88% | - | not teaching it |
| DIEPEVEEN, WILLEM reviews ↗ | 3.65 | 3.75 | 23 | 1 | 87% | - | 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.
What the work looks like
- No exams, said by 10 reviewers
- Problem sets mentioned, no number given, said by 5 reviewers
- 1 project, said by 3 reviewers
- Group works mentioned, no number given, said by 1 reviewer
Read from 24 reviews 26 days ago. dibs counts only what a review actually states, so a kind of work nobody mentioned is absent rather than zero.
What comes first
The chain dibs can see is 6 courses long before MATH 156. This is a floor, not a deadline: dibs has not read a class page for 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 Falls
Ran in 3 of the last 5 Falls, so it turns up in Fall sometimes rather than dependably.
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
Lecture, three hours; discussion, one hour. Requisites: courses 115A, 164, 170A or 170E or Statistics 100A, and Computer Science 31 or Program in Computing 10A. Strongly recommended requisite: Program in Computing 16A or Statistics 21. Introductory course on mathematical models for pattern recognition and machine learning. Topics include parametric and nonparametric probability distributions, curse of dimensionality, correlation analysis and dimensionality reduction, and concepts of decision theory. Advanced machine learning and pattern recognition problems, including data classification and clustering, regression, kernel methods, artificial neural networks, hidden Markov models, and Markov random fields. Projects in MATLAB to be part of final project presented in class. P/NP or letter grading.
Sections in Fall 2026
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 2 readings. Seat readings are snapshots, not live availability. Their timestamps show when dibs last checked.
-
Lec 1 MWF 11:00am-11:50amWaitlist
Martin, B.
3.51predicted, all instructors · 438 grades0 of 3 places taken. 40 of 40 enrolled high risk
1 discussion, none open. The way in is the lecture's waitlist, at 0 of 3.
Every discussion, with its time and seats (1)
-
Dis 1A
R 11:00am-11:50am · Boelter Hall 5436
Waitlist0 of 3 places taken.
40 of 40 enrolled
-
Final enrollment, averaged across each term's sections: between 32 and 45 a term over 8 terms, summer excluded, most recently 32 in Fall 2025. That is the shape across years, not this term, which is above.
From the people who took it
Student reviews
What taking MATH 156 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.
Seats over time in Fall 2026
How each lecture has filled since dibs first saw it.
A dot is a reading where something moved: the seat count, the status, or the waitlist.
What a flat run means, and where a line starts
Flat between dots means it did not drift, it held and then jumped, and a line starts when dibs first saw that lecture rather than reaching back before it existed. A lecture that has never moved draws a flat line across the whole window, and one with two or three readings draws a line joining them, which is too few to read a trend from.
Lec 1 · Martin, B. · 40 of 40 enrolled
Nothing read since 1 Oct, 3 hours ago. Over about 2 months before that, same seat count, but the status went closed to waitlist, the waitlist moved and came back, and the seat count moved and came back.
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.