Fall 2026

Machine Learning

COMPSCI/ECE 760 covers computational approaches to learning, including inductive inference, explanation-based learning, and cognitive modeling.

offering recorded3 credits
Recorded instructors · Fall 2026 Jerry Zhu3.3/5

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
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI/ECE 760 used by

“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 2026

Jerry Zhu

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 3.07/5 from 27 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 3.0/5 raw quality · 4.0/5 difficulty · 2 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

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.

Historical 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 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction70 / 1000
LEC 001Classroom Instruction9 / 1000

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

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

  • Inductive inference
  • Explanation-based learning
  • Analogical learning
  • Connectionism
  • Formal models of learning
  • The concept of learning
  • Cognitive modeling

Skills

  • Developing algorithms for learning
  • Comparing and evaluating learning algorithms
  • Applying cognitive modeling and psychological results

Grades

Historical instructor

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.

Download part 1
Selected offering source records
COMPSCI/ECE 760 · Fall 2026

Machine Learning

Recorded 2026-09-07
COMPSCI/ECE 760 · Fall 2026

Machine Learning

Recorded 2026-09-07
Raw 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
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
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"
}