CS 498

Fall 2026 All Classes

All Classes

Special Topics

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 2026
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
64850	Online Lecture	D13	10:00AM -10:50AM	MWF	n.a.	Alawini, A	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Data Management in the Cloud Section Info: Cloud computing has recently seen a lot of attention from research and industry for applications that can be parallelized on shared-nothing architectures and have a need for elastic scalability. As a consequence, new data management requirements have emerged with multiple solutions to address them. This course will look at the principles behind data management in the cloud as well as discuss actual cloud data management systems that are currently in use or being developed. The topics covered in the course range from novel data processing paradigms (MapReduce, Scope, DryadLINQ), to commercial cloud data management platforms (Google BigTable, Microsoft Azure, Amazon S3 and Dynamo, Yahoo PNUTS) and open-source NoSQL databases (Cassandra, MongoDB, Neo4J). The world of cloud data management is currently very diverse and heterogeneous. Therefore, our course will also report on efforts to classify, compare and benchmark the various approaches and systems. Students in this course will gain broad knowledge about the current state of the art in cloud data management and, through a course project, practical experience with a specific system. Recommended: CS 411 or a similar introductory database course. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
64851	Online Lecture	D14	10:00AM -10:50AM	MWF	n.a.	Alawini, A	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Data Management in the Cloud Section Info: Cloud computing has recently seen a lot of attention from research and industry for applications that can be parallelized on shared-nothing architectures and have a need for elastic scalability. As a consequence, new data management requirements have emerged with multiple solutions to address them. This course will look at the principles behind data management in the cloud as well as discuss actual cloud data management systems that are currently in use or being developed. The topics covered in the course range from novel data processing paradigms (MapReduce, Scope, DryadLINQ), to commercial cloud data management platforms (Google BigTable, Microsoft Azure, Amazon S3 and Dynamo, Yahoo PNUTS) and open-source NoSQL databases (Cassandra, MongoDB, Neo4J). The world of cloud data management is currently very diverse and heterogeneous. Therefore, our course will also report on efforts to classify, compare and benchmark the various approaches and systems. Students in this course will gain broad knowledge about the current state of the art in cloud data management and, through a course project, practical experience with a specific system. Recommended: CS 411 or a similar introductory database course. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
64848	Online Lecture	D1U	10:00AM -10:50AM	MWF	n.a.	Alawini, A	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Data Management in the Cloud Section Info: Cloud computing has recently seen a lot of attention from research and industry for applications that can be parallelized on shared-nothing architectures and have a need for elastic scalability. As a consequence, new data management requirements have emerged with multiple solutions to address them. This course will look at the principles behind data management in the cloud as well as discuss actual cloud data management systems that are currently in use or being developed. The topics covered in the course range from novel data processing paradigms (MapReduce, Scope, DryadLINQ), to commercial cloud data management platforms (Google BigTable, Microsoft Azure, Amazon S3 and Dynamo, Yahoo PNUTS) and open-source NoSQL databases (Cassandra, MongoDB, Neo4J). The world of cloud data management is currently very diverse and heterogeneous. Therefore, our course will also report on efforts to classify, compare and benchmark the various approaches and systems. Students in this course will gain broad knowledge about the current state of the art in cloud data management and, through a course project, practical experience with a specific system. Recommended: CS 411 or a similar introductory database course. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
42391	Discussion/ Recitation Online Lecture	D23 D23	10:00AM -10:50AM ARRANGED	F n.a.	406 200 S Wacker n.a.	Alawini, A Alawini, A	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Data Management in the Cloud Section Info: Cloud computing has recently seen a lot of attention from research and industry for applications that can be parallelized on shared-nothing architectures and have a need for elastic scalability. As a consequence, new data management requirements have emerged with multiple solutions to address them. This course will look at the principles behind data management in the cloud as well as discuss actual cloud data management systems that are currently in use or being developed. The topics covered in the course range from novel data processing paradigms (MapReduce, Scope, DryadLINQ), to commercial cloud data management platforms (Google BigTable, Microsoft Azure, Amazon S3 and Dynamo, Yahoo PNUTS) and open-source NoSQL databases (Cassandra, MongoDB, Neo4J). The world of cloud data management is currently very diverse and heterogeneous. Therefore, our course will also report on efforts to classify, compare and benchmark the various approaches and systems. Students in this course will gain broad knowledge about the current state of the art in cloud data management and, through a course project, practical experience with a specific system. Recommended: CS 411 or a similar introductory database course. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Restricted to MCS: Computer Sci OFF - UIUC.
50658	Discussion/ Recitation Online Lecture	D24 D24	10:00AM -10:50AM ARRANGED	F n.a.	406 200 S Wacker n.a.	Alawini, A Alawini, A	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Data Management in the Cloud Section Info: Cloud computing has recently seen a lot of attention from research and industry for applications that can be parallelized on shared-nothing architectures and have a need for elastic scalability. As a consequence, new data management requirements have emerged with multiple solutions to address them. This course will look at the principles behind data management in the cloud as well as discuss actual cloud data management systems that are currently in use or being developed. The topics covered in the course range from novel data processing paradigms (MapReduce, Scope, DryadLINQ), to commercial cloud data management platforms (Google BigTable, Microsoft Azure, Amazon S3 and Dynamo, Yahoo PNUTS) and open-source NoSQL databases (Cassandra, MongoDB, Neo4J). The world of cloud data management is currently very diverse and heterogeneous. Therefore, our course will also report on efforts to classify, compare and benchmark the various approaches and systems. Students in this course will gain broad knowledge about the current state of the art in cloud data management and, through a course project, practical experience with a specific system. Recommended: CS 411 or a similar introductory database course. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Restricted to MCS: Computer Sci OFF - UIUC. Not intended for First Time Freshman students.
42383	Discussion/ Recitation Online Lecture	D2U D2U	10:00AM -10:50AM ARRANGED	F n.a.	406 200 S Wacker n.a.	Alawini, A Alawini, A	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Data Management in the Cloud Section Info: Cloud computing has recently seen a lot of attention from research and industry for applications that can be parallelized on shared-nothing architectures and have a need for elastic scalability. As a consequence, new data management requirements have emerged with multiple solutions to address them. This course will look at the principles behind data management in the cloud as well as discuss actual cloud data management systems that are currently in use or being developed. The topics covered in the course range from novel data processing paradigms (MapReduce, Scope, DryadLINQ), to commercial cloud data management platforms (Google BigTable, Microsoft Azure, Amazon S3 and Dynamo, Yahoo PNUTS) and open-source NoSQL databases (Cassandra, MongoDB, Neo4J). The world of cloud data management is currently very diverse and heterogeneous. Therefore, our course will also report on efforts to classify, compare and benchmark the various approaches and systems. Students in this course will gain broad knowledge about the current state of the art in cloud data management and, through a course project, practical experience with a specific system. 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. Restricted to O/C Engineering City Scholars students.
70462	Discussion/ Recitation Online	K13 K13	ARRANGED 11:00AM -12:20PM	n.a. TR	Location Pending n.a.	Kang, D Kang, D	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Using and Understanding AI Section Info: You will learn what AI agents are, how to build them, and best practices for deploying them. This course will require programming and familiarity with the concept of application programming interfaces (APIs). There will be online and in person components. You are responsible for completing homeworks, quizzes, and any in person activities that are required. Prerequisites: One of CS 440, 441, 446 is highly recommended. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
70470	Discussion/ Recitation Online	K14 K14	ARRANGED 11:00AM -12:20PM	n.a. TR	Location Pending n.a.	Kang, D Kang, D	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Using and Understanding AI Section Info: You will learn what AI agents are, how to build them, and best practices for deploying them. This course will require programming and familiarity with the concept of application programming interfaces (APIs). There will be online and in person components. You are responsible for completing homeworks, quizzes, and any in person activities that are required. Prerequisites: One of CS 440, 441, 446 is highly recommended. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
70198	Discussion/ Recitation Online	K1U K1U	ARRANGED ARRANGED	n.a. n.a.	Location Pending n.a.	Kang, D Kang, D	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Using and Understanding AI Section Info: You will learn what AI agents are, how to build them, and best practices for deploying them. This course will require programming and familiarity with the concept of application programming interfaces (APIs). There will be online and in person components. You are responsible for completing homeworks, quizzes, and any in person activities that are required. Prerequisites: One of CS 440, 441, 446 is highly recommended. 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.
42449	Discussion/ Recitation Online	K23 K23	ARRANGED ARRANGED	n.a. n.a.	Location Pending n.a.	Kang, D Kang, D	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Using and Understanding AI Section Info: This section is intended for Chicago MCS only. There may be online and in person components. You are responsible for completing homeworks, quizzes, and any in person activities that are required. Please speak with your professor regarding expectations. All exams will be held in the Chicago CBTF in 200 S. Wacker Dr. Chicago. For up-to-date information about CS course restrictions, please see the following link: http:// go.cs.illinois.edu/csregister. You will learn what AI agents are, how to build them, and best practices for deploying them. This course will require programming and familiarity with the concept of application programming interfaces (APIs). Prerequisites: One of CS 440, 441, 446 is highly recommended. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to MCS: Computer Sci OFF - UIUC.
43368	Discussion/ Recitation Online	K24 K24	ARRANGED ARRANGED	n.a. n.a.	Location Pending n.a.	Kang, D Kang, D	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Using and Understanding AI Section Info: This section is intended for Chicago MCS only. There may be online and in person components. You are responsible for completing homeworks, quizzes, and any in person activities that are required. Please speak with your professor regarding expectations. All exams will be held in the Chicago CBTF in 200 S. Wacker Dr. Chicago. For up-to-date information about CS course restrictions, please see the following link: http:// go.cs.illinois.edu/csregister. You will learn what AI agents are, how to build them, and best practices for deploying them. This course will require programming and familiarity with the concept of application programming interfaces (APIs). Prerequisites: One of CS 440, 441, 446 is highly recommended. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to MCS: Computer Sci OFF - UIUC.
41988	Discussion/ Recitation Online	K2U K2U	ARRANGED ARRANGED	n.a. n.a.	Location Pending n.a.	Kang, D Kang, D	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Using and Understanding AI Section Info: This section is intended for Chicago City Scholars only. You will learn what AI agents are, how to build them, and best practices for deploying them. This course will require programming and familiarity with the concept of application programming interfaces (APIs). There may be online and in person components. You are responsible for completing homeworks, quizzes, and any in person activities that are required. Please speak with your professor regarding expectations. All exams will be held in the Chicago CBTF in 200 S. Wacker Dr. Chicago. Prerequisites: One of CS 440, 441, 446 is highly recommended. 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. Restricted to O/C Engineering City Scholars students.
40095	Online	KA3	ARRANGED	n.a.	n.a.	Kang, D	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Using and Understanding AI Section Info: This section is only for students that are in the Computer Science Online MCS/MCS-DS Program offered on the Coursera platform. Additional ProctorU fees may apply. You will learn what AI agents are, how to build them, and best practices for deploying them. This course will require programming and familiarity with the concept of application programming interfaces (APIs). Prerequisites: One of CS 440, 441, 446 is highly recommended. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC.
31533	Online	KA4	ARRANGED	n.a.	n.a.	Kang, D	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Using and Understanding AI Section Info: This section is only for students that are in the Computer Science Online MCS/MCS-DS Program offered on the Coursera platform. Additional ProctorU fees may apply. You will learn what AI agents are, how to build them, and best practices for deploying them. This course will require programming and familiarity with the concept of application programming interfaces (APIs). Prerequisites: One of CS 440, 441, 446 is highly recommended. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC.
65109	Lecture	LS3	9:30AM -10:45AM	MW	1310 Digital Computer Laboratory	Lai, F	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Machine Learning System Section Info: This course will introduce the basic concepts and cutting-edge practices in the design and implementation of efficient software systems for supporting machine learning (ML) models, with a particular focus on Generative AI (GenAI). By the end of the course, students will be able to: - Understand and critique the design principles behind state-of-the-art ML systems, from model architecture to system-level considerations. - Develop and utilize tools to profile, monitor, and optimize the performance of ML systems. - Explore and conduct research in topics related to the practical deployment and optimization of ML systems, contributing to the evolving landscape of efficient ML operations. Structure: The course will combine lectures, guest lectures from practioners, lab assignments, reading summaries, and a semester-long project. We will explore key ML topics from a systems perspective, addressing the relevant challenges across the ML lifecycle. Topics include, but are not limited to: - Basics of ML models from a systems perspective; - Systems for ML lifecycle (pre-training, training, fine-tuning, inference serving, and grounding). Note that this course is NOT focused on AI methods. Instead, we will focus on how one can build software systems so that existing AI methods can be used in practice and new AI methods can emerge. Prerequisites: Students are expected to have good programming skills and must have taken at least one systems-related course (from operating systems, databases, distributed systems, or networking). Having an ML/AI background is helpful 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 Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS:Computer Sci Online -UIUC, MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
70372	Lecture	LS4	9:30AM -10:45AM	MW	1310 Digital Computer Laboratory	Lai, F	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Machine Learning System Section Info: This course will introduce the basic concepts and cutting-edge practices in the design and implementation of efficient software systems for supporting machine learning (ML) models, with a particular focus on Generative AI (GenAI). By the end of the course, students will be able to: - Understand and critique the design principles behind state-of-the-art ML systems, from model architecture to system-level considerations. - Develop and utilize tools to profile, monitor, and optimize the performance of ML systems. - Explore and conduct research in topics related to the practical deployment and optimization of ML systems, contributing to the evolving landscape of efficient ML operations. Structure: The course will combine lectures, guest lectures from practioners, lab assignments, reading summaries, and a semester-long project. We will explore key ML topics from a systems perspective, addressing the relevant challenges across the ML lifecycle. Topics include, but are not limited to: - Basics of ML models from a systems perspective; - Systems for ML lifecycle (pre-training, training, fine-tuning, inference serving, and grounding). Note that this course is NOT focused on AI methods. Instead, we will focus on how one can build software systems so that existing AI methods can be used in practice and new AI methods can emerge. Prerequisites: Students are expected to have good programming skills and must have taken at least one systems-related course (from operating systems, databases, distributed systems, or networking). Having an ML/AI background is helpful 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 Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS:Computer Sci Online -UIUC, MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
70363	Lecture	LSU	9:30AM -10:45AM	MW	1310 Digital Computer Laboratory	Lai, F	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Machine Learning System Section Info: This course will introduce the basic concepts and cutting-edge practices in the design and implementation of efficient software systems for supporting machine learning (ML) models, with a particular focus on Generative AI (GenAI). By the end of the course, students will be able to: - Understand and critique the design principles behind state-of-the-art ML systems, from model architecture to system-level considerations. - Develop and utilize tools to profile, monitor, and optimize the performance of ML systems. - Explore and conduct research in topics related to the practical deployment and optimization of ML systems, contributing to the evolving landscape of efficient ML operations. Structure: The course will combine lectures, guest lectures from practioners, lab assignments, reading summaries, and a semester-long project. We will explore key ML topics from a systems perspective, addressing the relevant challenges across the ML lifecycle. Topics include, but are not limited to: - Basics of ML models from a systems perspective; - Systems for ML lifecycle (pre-training, training, fine-tuning, inference serving, and grounding). Note that this course is NOT focused on AI methods. Instead, we will focus on how one can build software systems so that existing AI methods can be used in practice and new AI methods can emerge. Prerequisites: Students are expected to have good programming skills and must have taken at least one systems-related course (from operating systems, databases, distributed systems, or networking). Having an ML/AI background is helpful 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	M14	2:00PM -3:15PM	TR	114 Transportation Building	Chekuri, C Erickson, J	Availability: Open (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: MCS Algorithms Section Info: MCS Algorithms: Analysis of algorithms, major paradigms of algorithm design including recursive algorithms, divide-and-conquer algorithms, dynamic programming, graph algorithms, greedy and local search, and reductions. Introduction to some advanced topics in algorithms including randomization and discrete optimization for combinatorial problems. One to two weeks on topics at the discretion of the instructor. Prerequisites: discrete mathematics, data structures, linear algebra and probability at the undergraduate level. Intended audience: graduate students who have not had a design- and proof-based algorithms course at the undergraduate level. This is in contrast to CS 473 which is suitable for those who have such a course. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Science -UIUC. Not intended for First Time Freshman students.
49172	Discussion/ Recitation Online Lecture	M24 M24	2:00PM -3:15PM ARRANGED	F n.a.	ARR Illini Center n.a.	Chekuri, C Erickson, J Chekuri, C Erickson, J	Availability: Open (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: MCS Algorithms Section Info: Chicago MCS Algorithms: Analysis of algorithms, major paradigms of algorithm design including recursive algorithms, divide-and-conquer algorithms, dynamic programming, graph algorithms, greedy and local search, and reductions. Introduction to some advanced topics in algorithms including randomization and discrete optimization for combinatorial problems. One to two weeks on topics at the discretion of the instructor. Prerequisites: discrete mathematics, data structures, linear algebra and probability at the undergraduate level. Intended audience: graduate students in the Chicago MCS program who have not had a design- and proof-based algorithms course at the undergraduate level. This is in contrast to CS 473 which is suitable for those who have such a course. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Restricted to MCS: Computer Sci OFF - UIUC. Not intended for First Time Freshman students.
49193	Lecture-Discussion	RT3	5:00PM -6:15PM	TR	0216 Siebel Center for Comp Sci	Amato, N Solis Vidana, I	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Robotics Team Project Section Info: This course consists of robotics team projects carried out in simulation and on physical robots, with project tracks such as F1TENTH autonomous driving and RoboCup 2D/3D soccer simulation. Students work in teams to implement and integrate algorithms for perception, localization, motion planning, and control into a complete robotic system that addresses a well-defined robotics challenge drawn from real-world or competition-inspired scenarios. Through hands-on laboratory work and iterative development, students gain strong technical competencies as well as strategic thinking and teamwork skills essential for complex robotics projects. Evaluation is based on demonstrating a fully working robotic system and on a written report and presentation describing the project design, implementation, and results. Prerequisites: Ability to program, CS 124 or CS 101; Data structures (CS 225) is recommended. Calculus 1 and Linear Algebra preferred. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Science -UIUC, MS:Computer Science -UIUC, PHD:Computer Science -UIUC, MS:CS:BS/MS Program - UIUC, MCS:BS/MCS Computer Sci -UIUC, or MENG:Engr:AutonomyRobotic-UIUC. Not intended for First Time Freshman students.
70185	Lecture-Discussion	RT4	5:00PM -6:15PM	TR	0216 Siebel Center for Comp Sci	Amato, N Solis Vidana, I	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Robotics Team Project Section Info: This course consists of robotics team projects carried out in simulation and on physical robots, with project tracks such as F1TENTH autonomous driving and RoboCup 2D/3D soccer simulation. Students work in teams to implement and integrate algorithms for perception, localization, motion planning, and control into a complete robotic system that addresses a well-defined robotics challenge drawn from real-world or competition-inspired scenarios. Through hands-on laboratory work and iterative development, students gain strong technical competencies as well as strategic thinking and teamwork skills essential for complex robotics projects. Evaluation is based on demonstrating a fully working robotic system and on a written report and presentation describing the project design, implementation, and results. Prerequisites: Ability to program, CS 124 or CS 101; Data structures (CS 225) is recommended. Calculus 1 and Linear Algebra preferred. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Science -UIUC, MS:Computer Science -UIUC, PHD:Computer Science -UIUC, MS:CS:BS/MS Program - UIUC, MCS:BS/MCS Computer Sci -UIUC, or MENG:Engr:AutonomyRobotic-UIUC. Not intended for First Time Freshman students.
49192	Lecture-Discussion	RTU	5:00PM -6:15PM	TR	0216 Siebel Center for Comp Sci	Amato, N Solis Vidana, I	Availability: Closed Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Robotics Team Project Section Info: Open to all continuing students in either a blended CS program or an Engineering program. This course consists of robotics team projects carried out in simulation and on physical robots, with project tracks such as F1TENTH autonomous driving and RoboCup 2D/3D soccer simulation. Students work in teams to implement and integrate algorithms for perception, localization, motion planning, and control into a complete robotic system that addresses a well-defined robotics challenge drawn from real-world or competition-inspired scenarios. Through hands-on laboratory work and iterative development, students gain strong technical competencies as well as strategic thinking and teamwork skills essential for complex robotics projects. Evaluation is based on demonstrating a fully working robotic system and on a written report and presentation describing the project design, implementation, and results. Prerequisites: Ability to program, CS 124 or CS 101; Data structures (CS 225) is recommended. Calculus 1 and Linear Algebra preferred. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
43753	Lecture	SE3	2:00PM -3:15PM	TR	3018 Campus Instructional Facility	Herman, G	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Open Source Software for Educa Section Info: This project-based course will teach students about open-source software engineering practices applied to education contexts. Students will learn about how to navigate issues and needs of an open-source projects and how to make contributions to those projects. Students will learn how to engage with stakeholders, how to spec their proposed contributions, and how to document their contributions. Students will work in teams and learn how to create and review pull requests to support higher code quality. Student projects will work closely with faculty from University of Illinois and other institutions to develop software that those instructors can use to support their teaching. Prerequisite: CS 225. Everywhere: For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS:Computer Sci Online -UIUC, MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
52640	Lecture	SE4	2:00PM -3:15PM	TR	3018 Campus Instructional Facility	Herman, G	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Open Source Software for Educa Section Info: This project-based course will teach students about open-source software engineering practices applied to education contexts. Students will learn about how to navigate issues and needs of an open-source projects and how to make contributions to those projects. Students will learn how to engage with stakeholders, how to spec their proposed contributions, and how to document their contributions. Students will work in teams and learn how to create and review pull requests to support higher code quality. Student projects will work closely with faculty from University of Illinois and other institutions to develop software that those instructors can use to support their teaching. Prerequisite: CS 225. Everywhere: For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
43670	Lecture	SEU	2:00PM -3:15PM	TR	3018 Campus Instructional Facility	Herman, G	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Open Source Software for Educa Section Info: This project-based course will teach students about open-source software engineering practices applied to education contexts. Students will learn about how to navigate issues and needs of an open-source projects and how to make contributions to those projects. Students will learn how to engage with stakeholders, how to spec their proposed contributions, and how to document their contributions. Students will work in teams and learn how to create and review pull requests to support higher code quality. Student projects will work closely with faculty from University of Illinois and other institutions to develop software that those instructors can use to support their teaching. Prerequisite: CS 225. Everywhere: 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.
66321	Lecture-Discussion	SF3	12:30PM -1:45PM	TR	1302 Siebel Center for Comp Sci	Ringer, T	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Software Foundations Section Info: We will learn how to use a proof assistant to formally reason about logic and programming languages, following the Software Foundations book series. Prerequisites: Any one of: (1) CS 421, (2) experience with at least one functional programming language, or (3) any higher-level proof-based math class. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
71121	Lecture-Discussion	SF4	12:30PM -1:45PM	TR	1302 Siebel Center for Comp Sci	Ringer, T	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Software Foundations Section Info: We will learn how to use a proof assistant to formally reason about logic and programming languages, following the Software Foundations book series. Prerequisites: Any one of: (1) CS 421, (2) experience with at least one functional programming language, or (3) any higher-level proof-based math class. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
31530	Lecture-Discussion	SFU	12:30PM -1:45PM	TR	1302 Siebel Center for Comp Sci	Ringer, T	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Software Foundations Section Info: We will learn how to use a proof assistant to formally reason about logic and programming languages, following the Software Foundations book series. Prerequisites: Any one of: (1) CS 421, (2) experience with at least one functional programming language, or (3) any higher-level proof-based math class. Restriction(s): Not intended for Computer Engineering major(s). Restricted to Undergrad - Urbana-Champaign.
61482	Lecture	SW3	9:30AM -10:45AM	WF	0216 Siebel Center for Comp Sci	Wang, S	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Machine Perception Section Info: Machine perception is the ability of an embodied device to perceive, comprehend, and reason about the surrounding environment. This course will introduce students to foundational principles of geometric and statistical learning approaches for machine perception. Topics include sensing techniques (vision, motion, audio, touch), probabilistic state estimation, localization and mapping, 3D reconstruction, scene understanding, vision-language-action models. Students will implement, debug and test machine perception algorithms on different sensory data in Python through hands-on programming assignments. Prerequisite: CS225; one of CS441, CS446, CS445, or equivalent. Our goal is to attract more senior undergrads and offer a lecture-assignment-based course. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
47171	Lecture	SW4	9:30AM -10:45AM	WF	0216 Siebel Center for Comp Sci	Wang, S	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Machine Perception Section Info: Machine perception is the ability of an embodied device to perceive, comprehend, and reason about the surrounding environment. This course will introduce students to foundational principles of geometric and statistical learning approaches for machine perception. Topics include sensing techniques (vision, motion, audio, touch), probabilistic state estimation, localization and mapping, 3D reconstruction, scene understanding, vision-language-action models. Students will implement, debug and test machine perception algorithms on different sensory data in Python through hands-on programming assignments. Prerequisite: CS225; one of CS441, CS446, CS445, or equivalent. Our goal is to attract more senior undergrads and offer a lecture-assignment-based course. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
40094	Lecture	SWU	9:30AM -10:45AM	WF	0216 Siebel Center for Comp Sci	Wang, S	Availability: CrossListOpen (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Machine Perception Section Info: Machine perception is the ability of an embodied device to perceive, comprehend, and reason about the surrounding environment. This course will introduce students to foundational principles of geometric and statistical learning approaches for machine perception. Topics include sensing techniques (vision, motion, audio, touch), probabilistic state estimation, localization and mapping, 3D reconstruction, scene understanding, vision-language-action models. Students will implement, debug and test machine perception algorithms on different sensory data in Python through hands-on programming assignments. Prerequisite: CS225; one of CS441, CS446, CS445, or equivalent. Our goal is to attract more senior undergrads and offer a lecture-assignment-based course. 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.