Recent recorded grades — Fall 2025: 3.91 GPA, 95.4% A/AB (n=87 letter grades). Includes jointly taught sections.
Fall 2026
Introduction to Deep Learning and Generative Models
Introduction to deep learning and generative models connecting neural networks to statistical concepts.
Summary
1 / 6
Historical reviews of Yiqiao Zhong: Reviewers note low overall workload, though exams can be confusing if lecture concepts are not understood. One student found the difficulty manageable with effort, while another cited flexible deadlines and fine tests.
Grade history
↗ average GPA
letter grades
letter grades
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AB
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All recorded terms · compare terms & instructors
Prerequisites
Course mapMATH 320, 321, 340, 341, 345, 375, graduate/professional standing, or declared in Statistics VISP
This is a best-effort interpretation; check the catalog requirements above.
Professors
Fall 2026Recent recorded grades — Fall 2025: 3.91 GPA, 95.4% A/AB (n=87 letter grades). Includes jointly taught sections.
Historical instructors & teaching patterns
Historical reviews of Yiqiao Zhong: Yiqiao Zhong receives mixed reviews regarding lecture quality, with some students finding them unprepared and difficult to follow while one reviewer praises them. Grading is frequently described as arbitrary and lacking clear rubrics, particularly for projects, though one student found the exams easy. Workload is generally considered manageable, and flexibility with deadlines is noted by at least one reviewer.
BAIHENG CHEN is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule.
BENJAMIN LENGERICH is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule.
YIQIAO ZHONG is recorded teaching in Spring 2023, Spring 2024, Spring 2025, Spring 2026. 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 | 76 / 80 | 3 |
| LEC 002 | Classroom Instruction | 10 / 20 | 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
Historical reviews for Yiqiao Zhong describe lectures as unhelpful, vague, and difficult to follow, with arbitrary grading and missing rubrics. While one student found him flexible and nice, the majority report a frustrating experience requiring significant self-study.
Recent recorded grades — Spring 2025: 3.70 GPA, 90.9% A/AB (n=88 letter grades); Fall 2025: 3.91 GPA, 95.4% A/AB (n=87 letter grades); Spring 2026: 3.62 GPA, 92.2% A/AB (n=64 letter grades).
difficulty & workload
Historical reviews of Yiqiao Zhong: Reviewers note low overall workload, though exams can be confusing if lecture concepts are not understood. One student found the difficulty manageable with effort, while another cited flexible deadlines and fine tests.
Historical reviews of Yiqiao Zhong: Students report that lectures are often ineffective, necessitating independent learning. Some found the professor accommodating regarding deadlines, while others were frustrated by a lack of clarity in assignments and exams, though one review praised the lectures and exams.
Topics
Skills
Grades
Latest available · Spring 2026— not enough history to project Fall 2026.
Grade distribution · % of letter grades
A
AB
B
BC
C
D
F
Grades over time
Through Spring 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
Introduction to Deep Learning and Generative Models
Recorded 2026-09-07Raw records
[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:932:025597",
"course_id": "STAT 453",
"course_uid": "course_b9ca05f3e5db5af2eb30f64a",
"term_id": "1272",
"source_course_id": "025597",
"source_subject_id": "932",
"title": "Introduction to Deep Learning and Generative Models",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Not Applicable"
}
]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": "3b928b9e469c032827061a90373cb2263e1b2d35f2f4743c4f734666eac294e2",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}