Machine Learning Security and Privacy. 3 credits

Catalog description

Covers advanced topics in security and privacy of machine learning techniques, including differential privacy, data collection, adversarial machine learning, model watermarking, and formal verification. Students are expected to complete projects on technical topics related to the scope of the course. They will also get hands-on experience with frameworks such as TensorFlow and Tensorflow Lite. The course will emphasize research skills, such as analyzing research papers, giving technical presentations, and writing summaries and reviews. Offered by Electrical & Comp. Engineering . May not be repeated for credit.