ECE 692 - Adversarial Learning
Adversarial learning is a new research area at the intersection of machine learning, artificial
intelligence, security, and digital forensics. The aim of this special topics class is to introduce
advanced graduate students to the selection of fundamental adversarial topics through focused
lectures, reviews of state-of-the-art topics, and hands-on projects. The topics covered in
this class will cover general adversarial perturbations, data, poisoning, bias, and
misinformation.
The class will spend about 10% of the time on lectures and paper reviews, and 90% on the
group research projects, where each group will focus on one research area, and be expected to
complete one to two research reports/papers.
Topics
- Adversarial perturbations
- Data poisoning
- Bias and Fairness
- Misinformation
- Vulnerabilities of AI/ML models
- Remediation and robustness measures
Assignments
- Attendance and presentations - 10% of the grade
- 1-2 peer-reviewed quality papers - 90% of the grade
Course Logistics
All the reading materials, assignments and discussions will be hosted on the course Canvas web page. We have access to Google Cloud Resources through the Google Credits for Education grant.