Fall 2026

Introduction to Deep Learning and Generative Models

Introduction to deep learning and generative models connecting neural networks to statistical concepts.

offering recorded3 credits
Recorded instructors · Fall 2026 Baiheng ChenBenjamin Lengerich

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

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 320, 321, 340, 341, 345, 375, graduate/professional standing, or declared in Statistics VISP

STAT 453

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Fall 2026

Recent recorded grades — Fall 2025: 3.91 GPA, 95.4% A/AB (n=87 letter grades). Includes jointly taught sections.

Recent 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 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction76 / 803
LEC 002Classroom Instruction10 / 200

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

Meeting source records
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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

  • Predictive modeling
  • Deep generative models
  • Artificial neural networks

Skills

  • Implementing deep neural networks
  • Applying generative models
  • Supervised learning tasks

Grades

Latest available · Spring 2026— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

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.

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Selected offering source records
STAT 453 · Fall 2026

Introduction to Deep Learning and Generative Models

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": "1272:932:025597",
    "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
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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": "3b928b9e469c032827061a90373cb2263e1b2d35f2f4743c4f734666eac294e2",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}