CS 442

Spring 2025 All Classes

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Trustworthy Machine Learning

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Credit: 3 OR 4 hours.

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, including 1) developing a machine learning classifier, 2) designing adversarial attacks against the built classifier, and 3) developing defenses to improve the robustness of the trained classifier against designed attacks. Students registered for 4 credit hours will also finish a final project based on the class topics, demonstrating their ability to propose related new algorithms based on the class subjects.

3 undergraduate hours. 4 graduate hours. Prerequisite: CS 225; one of CS 440, ECE 448, CS 441, CS 446 or ECE 449; one of MATH 225, MATH 257, MATH 415, MATH 416, ASRM 406 or BIOE 210.

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CS 442 class schedule data for spring 2025
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	73229	Lecture-Discussion	TMG	2:00PM -3:15PM	TR	1302 Siebel Center for Comp Sci	Zhao, H	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Info: CS 442 – Han Zhao: Course Description: <br/> As machine learning (ML) systems and platforms are increasingly being deployed in real-world applications, especially those in high-stakes domains, e.g., credit scoring, criminal justice, predictive policing, hiring decisions, etc., it is critical to ensure that these systems are behaving responsibly and are trustworthy. To this end, there has been growing interest from researchers and practitioners to develop and deploy ML models and algorithms that are not only accurate, but also fair, interpretable, robust and privacy-preserving. This broad area of research is commonly referred to as trustworthy ML. <br/> This course will cover topics within the broad area of trustworthy ML, including algorithmic fairness, model interpretability, model robustness to distributional shift, adversarial robustness, and differential privacy. Prerequisites include probability and statistics, linear algebra and calculus. The course will be self-contained, and existing knowledge about machine learning algorithms is preferred but not required. <br/> For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/CSregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
	73228	Lecture-Discussion	TMU	2:00PM -3:15PM	TR	1302 Siebel Center for Comp Sci	Zhao, H	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 3 hours Section Info: For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/CSregister Restriction(s): Restricted to Undergrad - Urbana-Champaign.