Spring 2027

Introduction to Deep Learning and Generative Models

Covers deep neural networks and generative models with statistical connections, emphasizing practical implementation via open-source computing for supervised learning and dataset synthesis.

offering recorded3 credits

Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Yiqiao Zhong

One student noted the semester had little workload and was manageable if concepts were understood, while another described exams as easy. Difficulty ratings varied, ranging from 2 to 5.

What you’ll be able to do

Develop an advanced understanding of deep learning and generative models, which represent state-of-the-art approaches for predictive modeling in today’s data-driven world.
Identify scenarios where it makes sense to deep learning for real-world problem-solving.
Build a repertoire of different algorithms and approaches to deep learning and understanding their various strengths and weaknesses.
Employ the Python programming language and Python’s scientific computing stack for implementing deep learning algorithms to 1) enhance the learning experience, 2) conduct research and be able to develop novel algorithms, and 3) apply deep learning to problem-solving in various fields and application areas.
Apply both the theoretical and practical concepts taught in this class to creative, real-world problem solving and communicating the outcome professionally in form of a scientific paper and a formal oral presentation.

Source: UW Guide

Grade history

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average GPA
letter grades

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Course details

Credits3 credits

Typically offeredNot Applicable

Subjects

Statistics

About this course

Deep learning is a field that specializes in discovering and extracting intricate structures in large, unstructured datasets for parameterizing artificial neural networks with many layers. Since deep learning has pushed the state-of-the-art in many research and application areas, it's become indispensable for modern technology. Focuses on a understanding deep, artificial neural networks by connecting it to related concepts in statistics. Beyond covering deep learning models for predictive modeling, focus on deep generative models. Besides explanations on a mathematical and conceptual level, emphasize the practical aspects of deep learning. Open-source computing provides hands-on experience for implementing deep neural nets, working on supervised learning tasks, and applying generative models for dataset synthesis.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

Section	Mode	Enrolled / capacity	Waitlist
LEC 001	Classroom Instruction	0 / 75	0
LEC 002	Classroom Instruction	0 / 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

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Student experience

the class

Past student reviews · Yiqiao Zhong

Students described lectures as difficult to follow and often vague, with some finding the material better suited for self-study. Opinions on the instructor's effectiveness varied significantly, with many citing communication challenges.

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

Past student reviews · Yiqiao Zhong

One student noted the semester had little workload and was manageable if concepts were understood, while another described exams as easy. Difficulty ratings varied, ranging from 2 to 5.

Past student reviews · Yiqiao Zhong

Feedback highlighted frustration with arbitrary grading and the absence of clear rubrics for group projects. While one student found the instructor flexible with deadlines, others felt instructions were too vague to follow.

Topics

Deep artificial neural networks and their connection to statistics.
Deep learning models for predictive modeling and deep generative models.
Practical implementation of deep neural nets, supervised learning tasks, and generative models for dataset synthesis.

Skills

Implementing deep learning algorithms using Python and its scientific computing stack.
Communicating outcomes professionally through scientific papers and formal oral presentations.

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

1280 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 · Spring 2027

Introduction to Deep Learning and Generative Models

Recorded 2026-10-14

Raw records

[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:932:025597",
    "course_id": "STAT 453",
    "course_uid": "course_b9ca05f3e5db5af2eb30f64a",
    "term_id": "1274",
    "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.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Model outputs & technical records

Qwen/Qwen3.8-27B

LLM outputs across runs

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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance

{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "d217d1a95f1d6c1da323ecc446fcbe6f25e77f3b187230c3313f82b987d25900",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}

Introduction to Deep Learning and Generative Models

Summary

What you’ll be able to do

Grade history

Prerequisites

Professors

Calendar & sections

Student experience

the class

difficulty & workload

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Where this course fits relative to

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Student reviews

Grade history

Introduction to Deep Learning and Generative Models

Summary

What you’ll be able to do

Grade history

Prerequisites

Professors

Calendar & sections

Student experience

the class

difficulty & workload

Topics

Skills

Grades

Grade distribution · % of letter grades

Grades over time

Where this course fits relative to UW–Madison

Sources & history

Catalog & offerings

Student reviews

Grade history

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