Fall 2024 AI Topic Course: Trustworthy AI Foundations
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Lectures: Wednesday 12:10-1:30 and Friday 2-3:20pm in Richard Weeks Hall on Busch campus room 208
Instructor: Ruixiang Tang
Office Hours: Friday 3-4:00pm, Hill Center room 416
Course Overview
This graduate topic course aims to give students a broader view of Trustworthy AI and focuses on understanding advanced techniques. The course covers key topics such as adversarial attacks and defenses, bias detection and mitigation, AI privacy, uncertainty estimation, and interpretable AI. Students will engage with the latest research, participate in discussions, and develop skills in critically analyzing and presenting complex material.
Prerequisites: This course will assume fundamental knowledge in AI and machine learning (e.g., 01:198:440 - Introduction to Artificial Intelligence, 16:198:536 - Machine Learning, or equivalent) and mathematical maturity (comfortable with linear algebra, probability, or equivalent). Students are expected to read and discuss research papers. Please contact the instructor if you have questions regarding whether your background is suitable for the course.
Grading
Grading
20% Quizzes
80% Final Project
We will have 5 short quizzes and one final project. The final project can be done individually or in groups of no more than 3. Presentations will take place during the last three weeks of the semester, followed by a Q&A session.
For the final project, students are required to choose a research paper on trustworthy AI, independently replicate its results, and critically analyze it to identify any limitations or potential areas for improvement. Students will then propose and develop a solution to address the identified issue or introduce a novel idea to enhance the research.
Course Schedule (tentative)
Course Schedule (tentative)
Week#
Topic
Notes
Recommended Papers for Further Reading
Week 1
Introduction to Trustworthy AI
Overview of course objectives
Importance of Trustworthy AI
Key concepts and definitions
Week 2
Foundational Concepts in Deep Learning
Basic Knowledge of Deep Learning
Feedforward, Backpropagation
MLP, CNN, RNN,Transformer
Week 3
Interpretable AI - Part 1
Introduction to Interpretable AI
Challenges of interpretability
Techniques for XAI
Week 4
Interpretable AI - Part 2
Advanced methods for XAI
Case studies and applications
Evaluation of interpretability
Week 5
Bias Detection and Mitigation for AI Models- Part 1
Understanding bias in AI models
Sources of bias
Methods for detecting bias
Week 6
Bias Detection and Mitigation for AI Models - Part 2
Techniques for mitigating bias
Fairness in AI
Ethical considerations
Week 7
Adversarial Attacks and Defenses for AI Models - Part 1
Introduction to adversarial attacks
Types of adversarial attacks
Case studies and examples
Week 8
Adversarial Attacks and Defenses for AI Models - Part 2
Defense against adversarial attacks
Evaluation of defense strategies
Practical applications and challenges
Week 9
AI Privacy
Introduction to AI privacy
Privacy Attack
Privacy-preserving techniques
Week 10
Trustworthy LLM - Part 1
Safety Alignment
Jailbreaking Attack and Defense
Multimodal Attack and Defense
Week 11
Trustworthy LLM - Part 2
Hallucination Detection and Mitigation
Uncertainty Estimation
Week 12
Guest Lecture / Industry Speaker
Invited talk from a leading expert in Trustworthy AI
Discussion and Q&A session
Week 13
Student Presentations - Part 1
Student presentations
Discussion and feedback
Week 14
Student Presentations - Part 2
Student presentations
Discussion and feedback
Week 15
Student Presentations - Part 3
Student presentations
Discussion and feedback
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