CS 498

Fall 2022 Part of Term 1

Part of Term 1
Aug 22-Dec 7

Special Topics

SHOW FINAL EXAM SCHEDULE

Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary.

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CS 498 class schedule data for fall 2022
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
63295	Lecture-Discussion	ABG	9:30AM -10:45AM	TR	Siebel Center for Comp Sci	Chekuri, C	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Algorithms for Big Data Section Info: This course will describe some algorithmic techniques that have been developed for handling large amounts of data which may not fit in memory or is available in limited ways. Topics include data stream algorithms, sampling and sketching techniques, sparsification methods, and parallelization with applications to signals, matrices, and graphs. Emphasis will be on theoretical aspects of the design and analysis of such algorithms. Strongly suggested Prerequisites: grades of at least B+ in CS 374 and CS 361, or comparable understanding and facility with algorithms and probability. 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.
63294	Lecture-Discussion	ABU	9:30AM -10:45AM	TR	Siebel Center for Comp Sci	Chekuri, C	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Algorithms for Big Data Section Info: This course will describe some algorithmic techniques that have been developed for handling large amounts of data which may not fit in memory or is available in limited ways. Topics include data stream algorithms, sampling and sketching techniques, sparsification methods, and parallelization with applications to signals, matrices, and graphs. Emphasis will be on theoretical aspects of the design and analysis of such algorithms. Strongly suggested Prerequisites: grades of at least B+ in CS 374 and CS 361, or comparable understanding and facility with algorithms and probability. 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.
70185	Lecture	AS3	3:30PM -4:45PM	TR	Campus Instructional Facility	Chandrasekharan, E	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Anti-Social Computing Section Info: https://courses.illinois.edu/schedule/2022/spring/CS/498 In this course, we will explore recent advances in detecting and discouraging antisocial behavior on the Internet. Focusing on a combination of sociological foundations and recent advances in HCI, NLP, and human-centered AI, we will examine online moderation through three lenses: understanding, building, and evaluating. First, we will survey the large spectrum of abusive behavior prevalent on the Internet and understand how current research defines such behavior. Next, we will examine existing moderation tools built using computational techniques and social computing theory. Finally, we will review experimental studies, surveys and real-time deployments that evaluate the efficacy of moderation strategies. Through this course, students will read and critique high-impact research papers, lead class discussions, engage with peers, brainstorm research ideas, learn to provide and receive constructive peer-feedback, and execute new research ideas for their class project. 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.
49193	Lecture	ASG	3:30PM -4:45PM	TR	Campus Instructional Facility	Chandrasekharan, E	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Anti-Social Computing Section Info: https://courses.illinois.edu/schedule/2022/spring/CS/498 In this course, we will explore recent advances in detecting and discouraging antisocial behavior on the Internet. Focusing on a combination of sociological foundations and recent advances in HCI, NLP, and human-centered AI, we will examine online moderation through three lenses: understanding, building, and evaluating. First, we will survey the large spectrum of abusive behavior prevalent on the Internet and understand how current research defines such behavior. Next, we will examine existing moderation tools built using computational techniques and social computing theory. Finally, we will review experimental studies, surveys and real-time deployments that evaluate the efficacy of moderation strategies. Through this course, students will read and critique high-impact research papers, lead class discussions, engage with peers, brainstorm research ideas, learn to provide and receive constructive peer-feedback, and execute new research ideas for their class project. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregiste Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
49192	Lecture	ASU	3:30PM -4:45PM	TR	Campus Instructional Facility	Chandrasekharan, E	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Anti Social Computing Section Info: https://courses.illinois.edu/schedule/2022/spring/CS/498 In this course, we will explore recent advances in detecting and discouraging antisocial behavior on the Internet. Focusing on a combination of sociological foundations and recent advances in HCI, NLP, and human-centered AI, we will examine online moderation through three lenses: understanding, building, and evaluating. First, we will survey the large spectrum of abusive behavior prevalent on the Internet and understand how current research defines such behavior. Next, we will examine existing moderation tools built using computational techniques and social computing theory. Finally, we will review experimental studies, surveys and real-time deployments that evaluate the efficacy of moderation strategies. Through this course, students will read and critique high-impact research papers, lead class discussions, engage with peers, brainstorm research ideas, learn to provide and receive constructive peer-feedback, and execute new research ideas for their class project. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregiste Restriction(s): Restricted to Undergrad - Urbana-Champaign.
61482	Lecture	GC3	2:00PM -3:15PM	WF	Siebel Center for Comp Sci	Chowdhary, G	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Mobile Robotics for CS Section Info: Principles of Mobile Robotics for Computer Scientists This course will introduce CS students to foundational principles of mobile robotics. Topics covered will be dynamic modeling, coordinate transformations, principles of operations of different sensors, sensor fusion algorithms including Kalman filters, introduction to Simultaneous Localization and Mapping, and introduction to feedback control for robotics. Prerequisite of CS 225 suggested. 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.
47171	Lecture	GCG	2:00PM -3:15PM	WF	Siebel Center for Comp Sci	Chowdhary, G	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Mobile Robotics for CS Section Info: Principles of Mobile Robotics for Computer Scientists This course will introduce CS students to foundational principles of mobile robotics. Topics covered will be dynamic modeling, coordinate transformations, principles of operations of different sensors, sensor fusion algorithms including Kalman filters, introduction to Simultaneous Localization and Mapping, and introduction to feedback control for robotics. Prerequisite of CS 225 suggested. 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.
40094	Lecture	GCU	2:00PM -3:15PM	WF	Siebel Center for Comp Sci	Chowdhary, G	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Mobile Robotics for CS Section Info: Principles of Mobile Robotics for Computer Scientists This course will introduce CS students to foundational principles of mobile robotics. Topics covered will be dynamic modeling, coordinate transformations, principles of operations of different sensors, sensor fusion algorithms including Kalman filters, introduction to Simultaneous Localization and Mapping, and introduction to feedback control for robotics. Prerequisite of CS 225 suggested. 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.
61457	Lecture	ML3	2:00PM -3:15PM	TR	Campus Instructional Facility	Zhao, H	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Trustworthy ML Section Info: 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. This course will cover topics within the broad area of trustworthy ML, including algorithmic fair- ness, 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. 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.
68912	Lecture	MLG	2:00PM -3:15PM	TR	Campus Instructional Facility	Zhao, H	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Trustworthy ML Section Info: 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. This course will cover topics within the broad area of trustworthy ML, including algorithmic fair- ness, 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. 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.
68911	Lecture	MLU	2:00PM -3:15PM	TR	Campus Instructional Facility	Zhao, H	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Trustworthy ML Section Info: 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. This course will cover topics within the broad area of trustworthy ML, including algorithmic fair- ness, 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. 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.
49838	Lecture	RC1	2:00PM -3:15PM	TR	Campus Instructional Facility	Cunningham, R	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 3 hours Section Title: Law &Policy Issues in CS Section Info: For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Law and Policy Issues In Computer Science This course will explore the intersection of public policy and computing technology. After a basic overview of the US legal system and administrative state, the course will examine the ways computing technology is regulated in areas such as privacy, crime, intellectual property, commerce, and national security. Students in the course will complete a series of technical projects related to legal issues, including scrutinizing digital rights management technology, evaluating digital forensics reports and expert testimony, and critiquing software patents. Students will also be expected to regularly read and respond to excerpts from relevant legal cases. Topics covered in the course will include Fourth and Fifth Amendment protections in cyberspace, network neutrality, antitrust, Section 230, cryptocurrency and digital property, espionage, and cyberwarfare. Prerequisite: CS 225 Restriction(s): Restricted to Undergrad - Urbana-Champaign.
31535	Lecture	RC2	2:00PM -3:15PM	TR	Campus Instructional Facility	Cunningham, R	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Law &Policy Issues in CS Section Info: For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Law and Policy Issues In Computer Science This course will explore the intersection of public policy and computing technology. After a basic overview of the US legal system and administrative state, the course will examine the ways computing technology is regulated in areas such as privacy, crime, intellectual property, commerce, and national security. Students in the course will complete a series of technical projects related to legal issues, including scrutinizing digital rights management technology, evaluating digital forensics reports and expert testimony, and critiquing software patents. Students will also be expected to regularly read and respond to excerpts from relevant legal cases. Topics covered in the course will include Fourth and Fifth Amendment protections in cyberspace, network neutrality, antitrust, Section 230, cryptocurrency and digital property, espionage, and cyberwarfare. Prerequisite of CS 225. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.