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Advanced topics in security and privacy problems in machine learning systems, selected from areas of current research such as: adversarial machine learning, differential privacy, game theory enabled defenses, robust learning methods, machine learning based cybercrime analysis, network intrusion detection, and malware analysis, and machine learning interpretation techniques. Course Information: 4 graduate hours. No professional credit. May be repeated if topics vary. Credit is not given towards a degree from multiple offerings of this course if those offerings have significant overlap, as determined by the CS department. Prerequisite: One of CS 446 or ECE 449 and one of CS 463 or ECE 424, or equivalent courses, by consent of instructor. Additional prerequisites or corequisites may be specified each term. See section information.