This graduate-level special topics course will be offered in Spring 2018. Meetings will be Fridays, 9:30-noon in Rice Hall 032. More information will be posted here later, but the seminar format will be loosely similar to what we used to TLSeminar last Spring.
This seminar will focus on understanding the risks adversaries pose to machine learning systems, and how to design more robust machine learning systems to mitigate those risks.
The seminar is open to ambitious undergraduate students (with instructor permission), and to graduate students interested in research in adversarial machine learning, privacy-preserving machine learning, fairness and transparency in machine learning, and other related topics. Previous background in machine learning and security is beneficial, but not required so long as you are willing and able to learn some foundational materials on your own.
For more information, contact David Evans.