ECE 2157 - DATA SCIENCE AND SECURITY

Minimum Credits: 3
Maximum Credits: 3
Long Description This course introduces data science and machine learning methods for securing modern systems, with emphasis on anomaly detection, adversarial machine learning, and data privacy. Students learn to identify unusual patterns in data and system behavior to detect unreliable data, rare events, and potential cyberattacks, while also examining how machine learning models can be attacked and how their robustness can be improved. The course covers extreme value analysis for modeling low-frequency, high-impact events; supervised, unsupervised, and semi-supervised machine learning approaches to anomaly detection; and privacy-preserving techniques, including data anonymization and differential privacy.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade
Course Requirements: PREQ: ECE-PHD or ECE-MSECE

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