Fall 2026

Advanced Deep Learning

Advanced deep learning methods including architecture design, generative modeling, and interpretability.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 4

Historical reviews of Yixuan Li: Yixuan Li is described as great by one reviewer.

Grade history

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

Prerequisites

Course map

E C E/​COMP SCI 760

COMPSCI 762

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

Prerequisite text tree

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews for Yixuan Li describe her as great. This single review indicates high quality, though no specific teaching strengths or concerns are detailed beyond this general praise.

YIXUAN LI is recorded teaching in Fall 2021, Fall 2022, Fall 2023, Fall 2025. 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

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SectionModeEnrolled / capacityWaitlist

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

Meeting source records

No records available.

Student experience

the class

Historical reviews of Yixuan Li: Yixuan Li is described as great by one reviewer.

Recent recorded grades — Fall 2022: 3.92 GPA, 97.8% A/AB (n=45 letter grades); Fall 2023: 3.90 GPA, 97.1% A/AB (n=34 letter grades); Fall 2025: 3.83 GPA, 91.3% A/AB (n=46 letter grades).

difficulty & workload

No workload feedback recorded.

Historical reviews of Yixuan Li: The reviewer rated the course quality highly.

Topics

  • Neural architecture design
  • Robustness and reliability
  • Learning with less supervision and lifelong learning
  • Deep generative modeling
  • Theoretical understanding
  • Interpretable deep learning

Skills

  • Neural architecture design and robustness analysis
  • Deep generative modeling and interpretability

Grades

Latest available · Fall 2025— 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 Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1320 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

No offering records for the selected term.

Raw records 
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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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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
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "e2dd44e241d03d0c555c89587a0e071e8b85e52578af0c7b2919fb213e06a33b",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}