Recommended Preparation: Content in this course assumes students already have a basic understanding of machine learning.
This is an intensive course on Trustworthy machine learning. The course covers different topics in emerging research areas related to the broader study of security and privacy in machine learning. Students will learn about attacks against computer systems leveraging machine learning, as well as defense techniques to mitigate such attacks.
Students will familiarize themselves with the emerging body of literature from different research communities investigating these questions. The class is designed to help students explore new research directions and applications. Most of the course readings will come from both seminal and recent papers in the field.
Grading will be based on paper presentation, paper summaries, class notes, participation, exam, and a research project. The goal of the course research project is to provide the students with an opportunity to explore research directions in trustworthy machine learning. The project should be related to the course content. An expected project consists of a novel and sound solution to an interesting problem, comprehensive literature review and discussion, thorough theoretical/experimental evaluation, and comparisons with existing approaches.
