Trustworthy Machine Learning

Fundamentals of trustworthy machine learning. Overview of modern machine learning techniques and the associated security, privacy, and data quality issues, adversarial machine learning (e.g., decision-time attacks & defenses, data poisoning attacks & defenses, robustness certification), privacy-preserving machine learning (e.g., membership inference attacks, model inversion attacks, differential privacy, and federated learning), strategic data collection and utilization (e.g., data valuation, data selection).