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Prepares students to understand the security and privacy problems in machine learning and educates students to propose different attack strategies to identify the vulnerabilities of a range of learning algorithms and understand different defense approaches towards trustworthy machine learning systems. Students will explore topics including basic machine learning foundations (e.g., linear regression and PCA), adversarial attacks against different learning algorithms, differential privacy, data valuation, and different categories of defenses. The lessons are reinforced via a series of topic-driven lectures, coding assignments, related paper readings, exams and in-class discussions. Students will learn to analyze current interactions between attackers and defenders on machine learning and therefore develop an understanding of the principles on trustworthy machine learning which is an emerging and important topic. Students will be required to finish three related homework projects, includi