Jerry Zhu receives polarized feedback regarding his teaching style. One reviewer criticizes his theoretical approach and unorganized notes, while another praises his ability to build intuition and explain complex mathematical theory clearly.
Fall 2026
Machine Learning
COMPSCI/ECE 760 covers computational approaches to learning, including inductive inference, explanation-based learning, and cognitive modeling.
Recorded instructors · Fall 2026 Jerry Zhu
Summary
1 / 6
One reviewer rated the difficulty as 5/5 and noted that students were expected to read PowerPoint slides to prepare for class.
Grade history
↗ average GPA
letter grades
letter grades
A
AB
B
BC
C
D
F
All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Graduate/professional standing
Professors
Fall 2026Historical instructors & teaching patterns
Jerry Zhu is the current instructor. The provided reviews describe historical instructors' teaching styles and course structures.
MARK CRAVEN is recorded teaching in Fall 2012, Fall 2013, Fall 2014, Fall 2015, Fall 2016, Spring 2018, Spring 2019. Recorded history may be incomplete and does not establish a future schedule.
YINGYU LIANG is recorded teaching in Fall 2017, Fall 2018, Spring 2020. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Fall 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 70 / 100 | 0 |
| LEC 001 | Classroom Instruction | 9 / 100 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Reviews of Jerry Zhu are polarized, with one calling him a gem who makes complex concepts accessible and another criticizing his theoretical approach and unorganized notes.
Recent recorded grades — Spring 2025: 3.58 GPA, 89.4% A/AB (n=66 letter grades); Fall 2025: 3.80 GPA, 98.1% A/AB (n=105 letter grades); Spring 2026: 3.61 GPA, 79.3% A/AB (n=58 letter grades).
difficulty & workload
One reviewer rated the difficulty as 5/5 and noted that students were expected to read PowerPoint slides to prepare for class.
A positive experience involved using the board and examples to build intuition, making underlying mathematical theory accessible.
Topics
Skills
Grades
Fall 2026 · Projected
Before grades are released– average GPA
Approximate 80% prediction interval
About this estimate
The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.
The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.
8 earlier forecasts · 0.12 GPA average error.
Grades over time
Through Fall 2026
More grade details Grade mix, volume & source data
Where this course fits relative to
Latest available grades · Spring 2026 · all course levels
GPA
Higher than % of other courses in this group.
Course GPAs · red marks this course’s range
letter grades
More recorded grades than % of other courses in this group.
Typical course in this group: letter grades.
About this comparison
1283 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.
Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.
Sources & history
UW–Madison
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
Machine Learning
Recorded 2026-09-07Machine Learning
Recorded 2026-09-07Raw records
[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:266:004331",
"course_id": "COMPSCI/ECE 760",
"course_uid": "course_821f9bd2b5a7758dd5db1e18",
"term_id": "1272",
"source_course_id": "004331",
"source_subject_id": "266",
"title": "Machine Learning",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Occasionally"
},
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:320:004331",
"course_id": "COMPSCI/ECE 760",
"course_uid": "course_821f9bd2b5a7758dd5db1e18",
"term_id": "1272",
"source_course_id": "004331",
"source_subject_id": "320",
"title": "Machine Learning",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Occasionally"
}
]Rate My Professors
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Madgrades
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "d8885892089bdc7d448cb7c57c86074d46959349f5e3bfd0beb7966c83a17606",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}