CS 498

Spring 2025 All Classes

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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 spring 2025
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
59276	Online	CCG	ARRANGED	n.a.	n.a.	Farivar, R	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Cloud Computing Applications Section Info: This course provides a comprehensive overview of modern cloud computing technologies, examining their foundational concepts, economic impact, and transformative potential in enterprise computing. It explores cloud service models, including Infrastructure as a Service, Platform as a Service, and Software as a Service, alongside serverless computing, Big Data programming with Apache Hadoop and Spark, and the role of cloud environments in deploying these systems. The curriculum delves into cloud storage systems, distributed key-value stores, in-memory databases, and advanced data management technologies such as NewSQL, NoSQL, and Spark SQL. Topics also include data analytics, machine learning, graph processing, and real-time data streaming systems like Apache Storm and Spark Streaming. Students gain insights into virtualization, containers, and orchestration tools such as Docker, Kubernetes, and Infrastructure as Code. <br/> This section is for "on campus" students. This course will be taught on the Coursera platform. Students taking CS courses on the Coursera platform for the first time must take additional steps to correctly setup their Coursera account and complete a brief onboarding course to gain access to the course. Students who enroll in this course must read “Instructions to access CS courses delivered on Coursera platform” available at http://go.cs.illinois.edu/CSregister, failure to follow these instructions will result in late course access. 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 MCS:Computer Sci Online -UIUC or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
69511	Online	CCU	ARRANGED	n.a.	n.a.	Farivar, R	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 3 hours Section Title: Cloud Computing Applications Section Info: This course provides a comprehensive overview of modern cloud computing technologies, examining their foundational concepts, economic impact, and transformative potential in enterprise computing. It explores cloud service models, including Infrastructure as a Service, Platform as a Service, and Software as a Service, alongside serverless computing, Big Data programming with Apache Hadoop and Spark, and the role of cloud environments in deploying these systems. The curriculum delves into cloud storage systems, distributed key-value stores, in-memory databases, and advanced data management technologies such as NewSQL, NoSQL, and Spark SQL. Topics also include data analytics, machine learning, graph processing, and real-time data streaming systems like Apache Storm and Spark Streaming. Students gain insights into virtualization, containers, and orchestration tools such as Docker, Kubernetes, and Infrastructure as Code. <br/> This section is for "on campus" students. This course will be taught on the Coursera platform. Students taking CS courses on the Coursera platform for the first time must take additional steps to correctly setup their Coursera account and complete a brief onboarding course to gain access to the course. Students who enroll in this course must read “Instructions to access CS courses delivered on Coursera platform” available at http://go.cs.illinois.edu/CSregister, failure to follow these instructions will result in late course access. 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.
68232	Online	CSP	9:30AM -10:45AM	TR	n.a.	Alawini, A	Part of Term: 1 Date Range: 01/21/25-05/07/25 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. Restriction(s): Restricted to Undergrad - Urbana-Champaign. Restricted to O/C Engineering City Scholars students.
51263	Online	DC3	9:30AM -10:45AM	TR	n.a.	Alawini, A	Part of Term: 1 Date Range: 01/21/25-05/07/25 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. Restriction(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.
41139	Online	DCG	9:30AM -10:45AM	TR	n.a.	Alawini, A	Part of Term: 1 Date Range: 01/21/25-05/07/25 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. Restriction(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.
39659	Online	DCU	9:30AM -10:45AM	TR	n.a.	Alawini, A	Part of Term: 1 Date Range: 01/21/25-05/07/25 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. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
65868	Online	DSO	ARRANGED	n.a.	n.a.	Farivar, R	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Cloud Computing Applications Section Info: This section is only for students that are in the Computer Science Online MCS/MCS-DS This course provides a comprehensive overview of modern cloud computing technologies, examining their foundational concepts, economic impact, and transformative potential in enterprise computing. It explores cloud service models, including Infrastructure as a Service, Platform as a Service, and Software as a Service, alongside serverless computing, Big Data programming with Apache Hadoop and Spark, and the role of cloud environments in deploying these systems. The curriculum delves into cloud storage systems, distributed key-value stores, in-memory databases, and advanced data management technologies such as NewSQL, NoSQL, and Spark SQL. Topics also include data analytics, machine learning, graph processing, and real-time data streaming systems like Apache Storm and Spark Streaming. Students gain insights into virtualization, containers, and orchestration tools such as Docker, Kubernetes, and Infrastructure as Code. <br/> Program offered on the Coursera platform. Additional ProctorU fees may apply. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC.
65904	Lecture-Discussion	LS3	2:00PM -3:15PM	TR	1214 Siebel Center for Comp Sci	Zhang, M	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 3 hours Section Title: Machine Learning System Section Info: The goal of this course is to provide students with an in-depth understanding of various elements of modern machine learning systems, ranging from the performance characteristics of ML models such as transformers and diffusers, performance optimization techniques that reduce the compute, memory, and communication for training and inference of large ML models, and compression algorithms that make ML models smaller and cheaper. The course will also conduct case studies on modern large language model training and serving and cover the design rationale behind state-of-the-art machine learning frameworks. Prerequisites: CS 425, CS 446 For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/ CSregister Restriction(s): Restricted to Electrical & Computer Engr or 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.
60162	Lecture-Discussion	LSG	2:00PM -3:15PM	TR	1214 Siebel Center for Comp Sci	Zhang, M	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Machine Learning System Section Info: The goal of this course is to provide students with an in-depth understanding of various elements of modern machine learning systems, ranging from the performance characteristics of ML models such as transformers and diffusers, performance optimization techniques that reduce the compute, memory, and communication for training and inference of large ML models, and compression algorithms that make ML models smaller and cheaper. The course will also conduct case studies on modern large language model training and serving and cover the design rationale behind state-of-the-art machine learning frameworks. Prerequisites: CS 425, CS 446 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 MCS:Computer Sci Online -UIUC, MCS: Computer Sci OFF - UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
61698	Lecture-Discussion	LSU	2:00PM -3:15PM	TR	1214 Siebel Center for Comp Sci	Zhang, M	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 3 hours Section Title: Machine Learning System Section Info: The goal of this course is to provide students with an in-depth understanding of various elements of modern machine learning systems, ranging from the performance characteristics of ML models such as transformers and diffusers, performance optimization techniques that reduce the compute, memory, and communication for training and inference of large ML models, and compression algorithms that make ML models smaller and cheaper. The course will also conduct case studies on modern large language model training and serving and cover the design rationale behind state-of-the-art machine learning frameworks. Prerequisites: CS 425, CS 446 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.
56905	Online	MC3	9:30AM -10:45AM	TR	n.a.	Alawini, A	Part of Term: 1 Date Range: 01/21/25-05/07/25 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. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS: Computer Sci OFF - UIUC. Not intended for First Time Freshman students.
56906	Online	MCS	9:30AM -10:45AM	TR	n.a.	Alawini, A	Part of Term: 1 Date Range: 01/21/25-05/07/25 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. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS: Computer Sci OFF - UIUC. Not intended for First Time Freshman students.
61923	Lecture-Discussion	QC3	9:30AM -10:45AM	TR	1304 Siebel Center for Comp Sci		Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 3 hours Section Title: Intro to Quantum Computing Section Info: This course aims to introduce the principles of quantum computing, laying a solid foundation for further advanced courses or research in quantum information. We will tentatively cover the following topics: - Basic concepts and axioms in quantum information, including what a qubit is, what entanglement means, and other related concepts - Fundamental computational operations like quantum gates and measurements - Exchanging quantum information through basic protocols like quantum teleportation and superdense coding - Solving computational problems using quantum algorithms such as Simons' algorithm, Quantum Fourier Transform and phase estimation, Shor's factoring algorithm, Grover search and amplitude amplification - Advanced topics possibly covering quantum complexity, cryptography, error correction, or more This course will take a theoretical computer science perspective on quantum computing. A background in quantum physics is not required, although it can be helpful. Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
40484	Lecture-Discussion	QCG	9:30AM -10:45AM	TR	1304 Siebel Center for Comp Sci	Granha Jeronimo, F Sinha, M	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Intro to Quantum Computing Section Info: This course aims to introduce the principles of quantum computing, laying a solid foundation for further advanced courses or research in quantum information. We will tentatively cover the following topics: - Basic concepts and axioms in quantum information, including what a qubit is, what entanglement means, and other related concepts - Fundamental computational operations like quantum gates and measurements - Exchanging quantum information through basic protocols like quantum teleportation and superdense coding - Solving computational problems using quantum algorithms such as Simons' algorithm, Quantum Fourier Transform and phase estimation, Shor's factoring algorithm, Grover search and amplitude amplification - Advanced topics possibly covering quantum complexity, cryptography, error correction, or more This course will take a theoretical computer science perspective on quantum computing. A background in quantum physics is not required, although it can be helpful. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
47231	Lecture-Discussion	QCU	9:30AM -10:45AM	TR	1304 Siebel Center for Comp Sci	Granha Jeronimo, F Sinha, M	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Intro to Quantum Computing Section Info: This course aims to introduce the principles of quantum computing, laying a solid foundation for further advanced courses or research in quantum information. We will tentatively cover the following topics: - Basic concepts and axioms in quantum information, including what a qubit is, what entanglement means, and other related concepts - Fundamental computational operations like quantum gates and measurements - Exchanging quantum information through basic protocols like quantum teleportation and superdense coding - Solving computational problems using quantum algorithms such as Simons' algorithm, Quantum Fourier Transform and phase estimation, Shor's factoring algorithm, Grover search and amplitude amplification - Advanced topics possibly covering quantum complexity, cryptography, error correction, or more This course will take a theoretical computer science perspective on quantum computing. A background in quantum physics is not required, although it can be helpful. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
31592	Laboratory-Discussion Online	SAG SAG	12:30PM -1:45PM ARRANGED	F n.a.	ARR Illini Center n.a.	Belabbas, M Belabbas, M	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Principles of Safe Autonomy Section Info: Introduces techniques for building autonomous systems such as autonomous cars, delivery drones, and manufacturing robots, and techniques for performing their safety analysis. Covers key algorithms and approaches in perception, modeling, motion planning, control, and safety analysis, with a view towards understanding their basic assumptions and performance guarantees. Also provides exposure to some of the state-of-the-art software tools for control, simulation, and analysis. Students will get experience through labs, programming assignments, and they will perform hands-on laboratory work on the Polaris GEM autonomous vehicle platform. Course material is distilled from recent research papers; thus, there is no required textbook. Weekly in-person lab/discussion meeting in 200 S. Wacker Dr. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS: Computer Sci OFF - UIUC.
31596	Laboratory-Discussion Online	SAU SAU	12:30PM -1:45PM ARRANGED	F n.a.	ARR Illini Center n.a.	Belabbas, M Belabbas, M	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Principles of Safe Autonomy Section Info: Introduces techniques for building autonomous systems such as autonomous cars, delivery drones, and manufacturing robots, and techniques for performing their safety analysis. Covers key algorithms and approaches in perception, modeling, motion planning, control, and safety analysis, with a view towards understanding their basic assumptions and performance guarantees. Also provides exposure to some of the state-of-the-art software tools for control, simulation, and analysis. Students will get experience through labs, programming assignments, and they will perform hands-on laboratory work on the Polaris GEM autonomous vehicle platform. Course material is distilled from recent research papers; thus, there is no required textbook. Weekly in-person lab/discussion meeting in 200 S. Wacker Dr. Restriction(s): Restricted to Undergrad - Urbana-Champaign. Restricted to O/C Engineering City Scholars students.
61697	Lecture-Discussion	SC3	3:30PM -4:45PM	TR	1302 Siebel Center for Comp Sci	Chandrasekharan, E Lambert, C Saha, K	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 3 hours Section Title: Computational Social Science Section Info: In this course, we will explore how social behaviors can be studied using large-scale datasets and computational methods. Focusing on a combination of sociological foundations and recent advances in NLP, human-centered AI, and HCI, we will learn to understand and analyze online social phenomena. Through this course, students will read and critique high-impact research papers, lead and engage in class discussions, provide and receive constructive peer-feedback, implement new research methods, and execute a new research project for their final paper. Restriction(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.
69420	Lecture-Discussion	SCG	3:30PM -4:45PM	TR	1302 Siebel Center for Comp Sci	Chandrasekharan, E Lambert, C Saha, K	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Computational Social Science Section Info: In this course, we will explore how social behaviors can be studied using large-scale datasets and computational methods. Focusing on a combination of sociological foundations and recent advances in NLP, human-centered AI, and HCI, we will learn to understand and analyze online social phenomena. Through this course, students will read and critique high-impact research papers, lead and engage in class discussions, provide and receive constructive peer-feedback, implement new research methods, and execute a new research project for their final paper. Restriction(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.
69419	Lecture-Discussion	SCU	3:30PM -4:45PM	TR	1302 Siebel Center for Comp Sci	Chandrasekharan, E Lambert, C Saha, K	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 3 hours Section Title: Computational Social Science Section Info: In this course, we will explore how social behaviors can be studied using large-scale datasets and computational methods. Focusing on a combination of sociological foundations and recent advances in NLP, human-centered AI, and HCI, we will learn to understand and analyze online social phenomena. Through this course, students will read and critique high-impact research papers, lead and engage in class discussions, provide and receive constructive peer-feedback, implement new research methods, and execute a new research project for their final paper. Restriction(s): Restricted to Undergrad - Urbana-Champaign.