Spring 2027

Trustworthy Artificial Intelligence

Trustworthy Artificial Intelligence explores security and privacy, covering differential privacy, algorithmic fairness, adversarial machine learning, and end-to-end trustworthy systems.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 3

Recent recorded grades — Fall 2020: 3.60 GPA, 75.0% A/AB (n=36 letter grades); Spring 2024: 3.90 GPA, 100.0% A/AB (n=21 letter grades); Spring 2026: 3.95 GPA, 100.0% A/AB (n=60 letter grades).

What you’ll be able to do

  • Evaluate machine learning and AI systems from an adversarial, security and privacy mindset.

  • Identify common pitfalls and problems in ensuring security and privacy for AI.

  • Summarize the commonalities and differences between notions of security and privacy (e.g., the difference between privacy and cryptographic security).

  • Explain the strengths and limitations of candidate definitions of robustness, security, privacy, and fairness properties in AI.

  • Apply useful primitives from end-to-end trustworthiness to machine learning and AI systems.

  • Use modern tools to design attacks and implement defensive measures.

Source: UW Guide 1 UW Guide 2

Grade history

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

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI/ECE 763

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

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

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

Recent recorded grades — Fall 2020: 3.60 GPA, 75.0% A/AB (n=36 letter grades); Spring 2024: 3.90 GPA, 100.0% A/AB (n=21 letter grades); Spring 2026: 3.95 GPA, 100.0% A/AB (n=60 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Differential privacy and algorithmic fairness
  • Adversarial machine learning
  • End-to-end trustworthy systems
  • Advanced topics in privacy, language-based security, and robust optimization

Skills

  • Evaluating ML/AI systems for security and privacy
  • Identifying AI security and privacy pitfalls
  • Designing attacks and implementing defensive measures

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

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

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

No offering records for the selected term.

Raw records 
[]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-29

View the dataset on Hugging Face
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": "c4ad4a7dd3ce80eadbad5796f1950ad81fea01390c8011d429736991b94a0c7b",
  "requirements_status": "valid",
  "dataset_revision": "c9cd06eaa45f9ddf7e69faa38b89ff3d3dd995dd",
  "observed_at": "2026-10-29 21:30:40.248904+00:00"
}