CMPS290P: Data Privacy via Machine Learning, and Back

Helps students achieve both expository knowledge and expertise in the field of data privacy. Focuses on fundamental techniques used in designing privacy-preserving, machine-learning systems in both academia and in the industry. Students are expected to read and understand recent research papers in the topic. Prerequisite(s): courses 201 and 242 or equivalent. Enrollment is restricted to graduate students.

5 credits

Year	Fall	Winter	Spring	Summer

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