MATH 156
Machine Learning
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 | Summer Session A 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. Read from Fall 2026, because dibs has not read a class page for MATH 156 in Summer Session A 2026 and a department can change what it requires. 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.
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 Summer Session A 2026
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.
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External reviews are separate and do not count toward the dibs rating.