Trustworthy Machine Learning

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Machine Learning (ML) is a subfield of Artificial Intelligence where computer algorithms are learning "by example", using past data. ML now replaces humans in many critical decision points and is used in various applications, such as banking and finance, image and speech processing, healthcare, and more. However, like traditional software, AI systems are often faulty and vulnerable to attacks. For example, Amazon had to scrap an AI-based recruiting tool that showed bias against women while Alexa and Siri were recently manipulated with hidden commands that humans cannot hear.

This seminar-style course will explore different topics in emerging research areas related to correctness, security, privacy, and fairness in machine learning. Students will learn about quality assurance methods for ML systems, attacks against ML systems, defense techniques to mitigate such attacks, and ethical implications of using ML systems.

The course assumes students already have a basic understanding of machine learning. Most of the course readings will come from both seminal and recent papers in the field. Each student will read, summarize, and present several scientific papers, as well as propose, implement, and present their own original project. As such, the course will also focus on polishing the students’ research, development, communication, and technical presentation skills.

Course Prerequisites

This course does not have formal prerequisites. However, previous programming experience and a basic understanding of machine learning (equivalent to CPSC 340, ELEC400M, or ELEC571M) are necessary.


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