CS 498

Spring 2021 Part of Term 1

Part of Term 1
Jan 25-May 5

Special Topics

SHOW FINAL EXAM SCHEDULE

Credit: 0 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 2021
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
66289	Online	AM1	ARRANGED	n.a.	n.a.	Morales Aguirre, M	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Applied Machine Learning Section Info: Techniques of machine learning, with applications to various signal problems. Techniques covered will be: regression including linear regression, multiple regression, regression forests and nearest neighbors regression; classification with various methods including logistic regression, support vector machines, nearest neighbors, simple boosting and decision forests; clustering with various methods including basic agglomerative clustering and k-means; resampling methods, including cross-validation and the bootstrap; model selection methods, including AIC, stepwise selection and the lasso; hidden Markov models; model estimation in the presence of missing variables; and neural networks, including deep networks. The course is intended to support students who wish to apply machine learning methods,and will focus on tool-oriented and problem-oriented exposition. Application areas include computer vision, natural language, interpreting accelerometer data, and understanding audio data. This course will be taught on the Coursera platform. This section will have one or more proctored online exams. Strongly suggested Prereq: A course in probability or statistics, a course in linear algebra, and some programming experience. 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 Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
65685	Online	AML	ARRANGED	n.a.	n.a.	Morales Aguirre, M	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Applied Machine Learning Section Info: Techniques of machine learning, with applications to various signal problems. Techniques covered will be: regression including linear regression, multiple regression, regression forests and nearest neighbors regression; classification with various methods including logistic regression, support vector machines, nearest neighbors, simple boosting and decision forests; clustering with various methods including basic agglomerative clustering and k-means; resampling methods, including cross-validation and the bootstrap; model selection methods, including AIC, stepwise selection and the lasso; hidden Markov models; model estimation in the presence of missing variables; and neural networks, including deep networks. The course is intended to support students who wish to apply machine learning methods, and will focus on tool-oriented and problem-oriented exposition. Application areas include computer vision, natural language, interpreting accelerometer data, and understanding audio data. This course will be taught on the Coursera platform. This section will have one or more proctored online exams. Strongly suggested Prereq: A course in probability or statistics, a course in linear algebra, and some programming experience 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.
67942	Online	AMO	ARRANGED	n.a.	n.a.	Morales Aguirre, M	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Applied Machine Learning Section Info: This course is only for students that are in the Computer Science MCS-DS Program. Additional Coursera ID verification and ProctorU fees may apply. Description:Techniques of machine learning, with applications to various signal problems. Techniques covered will be: regression including linear regression, multiple regression, regression forests and nearest neighbors regression; classification with various methods including logistic regression, support vector machines, nearest neighbors, simple boosting and decision forests; clustering with various methods including basic agglomerative clustering and k-means; resampling methods, including cross-validation and the bootstrap; model selection methods, including AIC, stepwise selection and the lasso; hidden Markov models; model estimation in the presence of missing variables; and neural networks, including deep networks. The course is intended to support students who wish to apply machine learning methods, and will focus on tool-oriented and problem-oriented exposition. Application areas include computer vision, natural language, interpreting accelerometer data, and understanding audio data. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
60221	Online	CA3	12:30PM -1:45PM	WF	n.a.	Sundaram, H	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Computational Advertising Section Info: This class will survey the emerging landscape of computational advertising. It will provide students with a thorough understanding of the technologies including web-search, auctions, behavioral targeting, mechanisms for viral marketing, that underpin the display of advertisements on a variety of locations. These locations include web pages (banner ads), on prominent search engines (text ads), on social media platforms, as well as cell phones. The students shall also learn about emerging areas in computational advertising including location-based adverting and algorithmic synthesis of personalized advertisements. Discussion around privacy will be a significant focus of the class. https://wiki.illinois.edu/wiki/display/CS498HS4SP21/ It is strongly suggested that you have the following before enrolling in the class; • (one of CS 173 or MATH 213) and • CS 225 and • one of MATH 225 or MATH 257 or MATH 415 or MATH 416 or ASRM 406) and • CS 361 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.
61715	Online	CA4	12:30PM -1:45PM	WF	n.a.	Sundaram, H	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Computaional Advertising Section Info: This class will survey the emerging landscape of computational advertising. It will provide students with a thorough understanding of the technologies including web-search, auctions, behavioral targeting, mechanisms for viral marketing, that underpin the display of advertisements on a variety of locations. These locations include web pages (banner ads), on prominent search engines (text ads), on social media platforms, as well as cell phones. The students shall also learn about emerging areas in computational advertising including location-based adverting and algorithmic synthesis of personalized advertisements. Discussion around privacy will be a significant focus of the class. https://wiki.illinois.edu/wiki/display/CS498HS4SP21/ It is strongly suggested that you have the following before enrolling in the class; • (one of CS 173 or MATH 213) and • CS 225 and • one of MATH 225 or MATH 257 or MATH 415 or MATH 416 or ASRM 406) and • CS 361 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 Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
47231	Online	DL3	11:00AM -12:15PM	MW	n.a.	Lazebnik, S	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Intro to Deep Learning Section Info: All class meetings will be online and synchronous. This course will provide an elementary hands-on introduction to neural networks and deep learning. Topics covered will include linear classifiers, multi-layer neural networks, back-propagation and stochastic gradient descent, convolutional neural networks, recurrent neural networks, generative networks, and deep reinforcement learning. Coursework will consist of programming assignments in TensorFlow or PyTorch. Those registered for 4 credit hours will have to complete a project. Prerequisites: multi-variable calculus, linear algebra, CS 361 or STAT 400. No previous exposure to machine learning is required. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
40484	Online	DL4	11:00AM -12:15PM	MW	n.a.	Lazebnik, S	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Intro to Deep Learning Section Info: All class meetings will be online and synchronous. This course will provide an elementary hands-on introduction to neural networks and deep learning. Topics covered will include linear classifiers, multi-layer neural networks, back-propagation and stochastic gradient descent, convolutional neural networks, recurrent neural networks, generative networks, and deep reinforcement learning. Coursework will consist of programming assignments in TensorFlow or PyTorch. Those registered for 4 credit hours will have to complete a project. Prerequisites: multi-variable calculus, linear algebra, CS 361 or STAT 400. No previous exposure to machine learning is required. 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.
68121	Online Lecture	DSG	12:30PM -1:50PM	MW	n.a.	Iyer, R	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Data Science & Analytics 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.
65868	Online	DSO	ARRANGED	n.a.	n.a.	Farivar, R	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Cloud Computing Applications Section Info: This course is only for students that are in the Computer Science MCS-DS Program. Additional ProctorU fees may apply. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
68120	Online Lecture	DSU	12:30PM -1:50PM	MW	n.a.	Iyer, R	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Data Science & Analytics Restriction(s): Restricted to Undergrad - Urbana-Champaign. Not intended for First Time Freshman students.
61720	Online	FCS	12:30PM -1:45PM	TR	n.a.	Williams, T	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Fundamentals of Comp Sci II Section Info: Introduction to the concepts and craft of computer science. It teaches students to both think and act like computer scientists. It changes how they approach problems and provide them with powerful tools that they can use to change the world. The course assumes no prior programming experience. This course is restricted to students in the iCAN program. Restriction(s): Restricted to NDEG:Computer Science -UIUC.
61719	Online	FOA	2:00PM -3:15PM	TR	n.a.	Gertner, Y	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Fundamentals of Algorithms II Section Info: Introduction to select topics of discrete mathematical frequently encountered in the study of Computer Science: counting, graphs, sets, functions, basics proofs, number theory, computability and introduction to algorithms. This class focuses on using these to model real world problems and utilize these techniques for problem solving. This course is restricted to students in the iCAN program. Restriction(s): Restricted to NDEG:Computer Science -UIUC.
55128	Online	IR1	9:30AM -10:45AM	TR	n.a.	Hauser, K	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: AI for Robot Manipulation Section Info: Theoretical foundations of motion planning and perception algorithms, and their implementation in modern robots. Topics include rigid transformations, forward and inverse kinematics, configuration space, motion planning, and task planning. State estimation, Kalman filters and their variants, visual sensing, and 3D mapping. Laboratory assignments will lead to implementation of intelligent autonomous behavior on a hardware platform. Undergrad section. 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.
55129	Online	IR2	9:30AM -10:45AM	TR	n.a.	Hauser, K	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: AI for Robot Manipulation Section Info: Theoretical foundations of motion planning and perception algorithms, and their implementation in modern robots. Topics include rigid transformations, forward and inverse kinematics, configuration space, motion planning, and task planning. State estimation, Kalman filters and their variants, visual sensing, and 3D mapping. Laboratory assignments will lead to implementation of intelligent autonomous behavior on a hardware platform. 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 Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
61928	Online	ISE	ARRANGED	n.a.	n.a.	Caesar, M	Part of Term: 1 Date Range: 01/25/21-05/05/21 Special Approval: Instructor Approval Required Credit: 4 hours Section Title: IOT for Software Engineering Section Info: Description: Students will gain exposure to software engineering principals through design and implementation of a large-scale cloud IoT software system. Students will gain real-world experience, working in teams to construct and refine a large software base. Each team will focus on a part of the software (graphical frontend, cloud backend, algorithm core, etc.) and will be expected to have or acquire the skills needed to contribute. We will work together to build something real; at the end of the semester, students will gain operational experience through deployment of their useful system on the Internet and make it available for users across the world. The focus of this class will be on cloud IoT and the development of software platforms that drive IoT applications, as opposed to hardware or wireless concepts covered in other courses. Prerequisites: solid programming experience (e.g., CS 241, 242, 438, 423), and consent of instructor (please email your CV to caesar@illinois.edu to request enrollment).
69725	Online	ITO	ARRANGED	n.a.	n.a.	Caesar, M	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Internet of Things Section Info: The Internet of Things (IoT) stands to be the next revolution in computing. Billions of data-spouting devices connected to the Internet are already fundamentally changing the way we live and work. This course teaches a deep understanding of IoT technologies from the ground up. Students will learn IoT device programming (Arduino and Raspberry Pi), sensing and actuating technologies, IoT protocol stacks (Zigbee, 5G, NFC, MQTT, etc), networking backhaul design and security enforcement, data science for IoT, and cloud-based IoT platforms such as AWS IoT. Students will be guided through laboratory assignments designed to give them practical real-world experience, where they will deploy a distributed wifi monitoring service, a cloud-based IoT service platform serving tens of thousands of heartbeat sensors, and more. Students will emerge from the class with a cutting-edge education on this rapidly emerging technology segment, and with the confidence to carry out tasks they will commonly encounter in industrial settings. This course is only for students that are in the Computer Science Online MCS Program. ProctorU fees may apply. Restricted to MCS:Computer Sci Online -UIUC. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
47219	Online	LB1	3:30PM -4:45PM	WF	n.a.	Li, B	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Trustworthy Machine Learning Section Info: Although machine learning has been widely applied to various applications, the security and privacy vulnerabilities of the models and algorithms require more careful exploration to develop trustworthy machine learning systems. This course will first discuss the foundation of machine learning, optimization algorithms, and deep learning models; and then introduce different attack approaches against various learning models. We will later discuss potential defense strategies and principles against different attacks, as well as how to protect data privacy to improve data utility for large scale learning systems in adversarial environments. 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.
47220	Online	LB2	3:30PM -4:45PM	WF	n.a.	Li, B	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Trustworthy Machine Learning Section Info: Although machine learning has been widely applied to various applications, the security and privacy vulnerabilities of the models and algorithms require more careful exploration to develop trustworthy machine learning systems. This course will first discuss the foundation of machine learning, optimization algorithms, and deep learning models; and then introduce different attack approaches against various learning models. We will later discuss potential defense strategies and principles against different attacks, as well as how to protect data privacy to improve data utility for large scale learning systems in adversarial environments. 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 Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
47232	Online	ME1	2:00PM -3:20PM	TR	n.a.	Ghose, S	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Arch. for Mobile & Edge Comp. Section Info: Mobile computing platforms (e.g., smartphones, tablets, wearables, processors in autonomous vehicles) have enabled a revolutionary change in society over the last two decades. Much of this success is a result of a fundamental shift in architectural design, with new architectures focusing on lowering energy consumption, improving the handling of data, and specializing for target platforms. These architectures are continuing to transform, as the relationship between data centers, networks, and mobile platforms changes, and as applications continue to deal with increasing amounts of data. This course will cover key enabling technologies and current research challenges for mobile computer architectures. Topics include smartphone architectures and hardware components, memory and storage systems for mobile platforms, system-on-chip integration, emerging applications, and edge computing platforms. The course will be taught using a combination of lectures and paper readings, and students will be expected to present research papers and complete a series of team projects (including a substantial final project). It is strongly suggested that students take: CS 433 or equivalent Restriction(s): Restricted to Undergrad - Urbana-Champaign.
65904	Online	ME2	2:00PM -3:20PM	TR	n.a.	Ghose, S	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Arch. for Mobile & Edge Comp. Section Info: Mobile computing platforms (e.g., smartphones, tablets, wearables, processors in autonomous vehicles) have enabled a revolutionary change in society over the last two decades. Much of this success is a result of a fundamental shift in architectural design, with new architectures focusing on lowering energy consumption, improving the handling of data, and specializing for target platforms. These architectures are continuing to transform, as the relationship between data centers, networks, and mobile platforms changes, and as applications continue to deal with increasing amounts of data. This course will cover key enabling technologies and current research challenges for mobile computer architectures. Topics include smartphone architectures and hardware components, memory and storage systems for mobile platforms, system-on-chip integration, emerging applications, and edge computing platforms. The course will be taught using a combination of lectures and paper readings, and students will be expected to present research papers and complete a series of team projects (including a substantial final project). It is strongly suggested that students take: CS 433 or equivalent 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.
39660	Online	PS3	11:00AM -12:15PM	TR	n.a.	Smaragdis, P	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Audio Computing Lab Section Info: This course will cover the computational foundations of modern audio applications. This will be a lab-like course in which students will be required to bring in their laptops in class and collectively implement a variety of core audio operations that are commonplace today. In this class we will cover the necessary theory to start working on audio processing, and implement a variety of applications such as room and 3D/virtual audio rendering, pitch manipulations and autotuning, denoising for communications and forensics, audio classification, music information retrieval based on audio, rudimentary speech recognition, speech and audio coding, applications of machine learning to audio scene recognition, audio restoration, missing data recovery, and many more. Students will need to have a good grasp of programming in Python (or MATLAB) and will be required to bring to class their laptops and headphones to participate in lab exercises. Suggested prerequisites include MATH416 (or equivalent) and CS241. 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.
67074	Online	PS4	11:00AM -12:15PM	TR	n.a.	Smaragdis, P	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Audio Computing Lab Section Info: This course will cover the computational foundations of modern audio applications. This will be a lab-like course in which students will be required to bring in their laptops in class and collectively implement a variety of core audio operations that are commonplace today. In this class we will cover the necessary theory to start working on audio processing, and implement a variety of applications such as room and 3D/virtual audio rendering, pitch manipulations and autotuning, denoising for communications and forensics, audio classification, music information retrieval based on audio, rudimentary speech recognition, speech and audio coding, applications of machine learning to audio scene recognition, audio restoration, missing data recovery, and many more. Students will need to have a good grasp of programming in Python (or MATLAB) and will be required to bring to class their laptops and headphones to participate in lab exercises. Suggested prerequisites include MATH416 (or equivalent) and CS241. 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 Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
47282	Lecture	RL1	2:00PM -3:15PM	TR	Location Pending	Jiang, N	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Reinforcement Learning Section Info: Reinforcement learning (RL) is a machine learning paradigm for sequential decision-making, which has enabled the recent successes in video/board game playing (e.g., AlphaGo). In this course we will introduce the fundamental concepts and some basic algorithms for RL. Most of the course will be highly mathematical, and the goal is to enable students to (1) understand the mathematical framework of RL, (2) tell what problems can be solved with RL, and how to express these problems using the RL formulation, (3) understand why and how RL algorithms are designed to work in theory, and (4) know how to experimentally and mathematically evaluate the effectiveness of an RL algorithm. There will be both programming and written assignments. Prerequisites: Required: Linear algebra (Math 415 or equivalent), Probability and Statistics (CS 361 or equivalent). Recommended: Numerical methods (CS 357 or 450), AI or Machine Learning (CS 440 and/or 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 Math & Computer Science or Computer Science or Statistics & Computer Science or Computer Sci & Anthropology or Computer Sci & Astronomy or Computer Sci & Chemistry or Computer Sci & Linguistics or Computer Science&Crop Sciences or Computer Science and Music or Computer Science & Economics or Computer Science & Advertising or Computer Science & Geog & GIS or Computer Science & Philosophy or Computer Sci & Animal Sci major(s). Restricted to Undergrad - Urbana-Champaign. Not intended for First Time Freshman students.
47283	Lecture	RL2	2:00PM -3:15PM	TR	Location Pending	Jiang, N	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Reinforcement Learning Section Info: Reinforcement learning (RL) is a machine learning paradigm for sequential decision-making, which has enabled the recent successes in video/board game playing (e.g., AlphaGo). In this course we will introduce the fundamental concepts and some basic algorithms for RL. Most of the course will be highly mathematical, and the goal is to enable students to (1) understand the mathematical framework of RL, (2) tell what problems can be solved with RL, and how to express these problems using the RL formulation, (3) understand why and how RL algorithms are designed to work in theory, and (4) know how to experimentally and mathematically evaluate the effectiveness of an RL algorithm. There will be both programming and written assignments. Prerequisites: Required: Linear algebra (Math 415 or equivalent), Probability and Statistics (CS 361 or equivalent). Recommended: Numerical methods (CS 357 or 450), AI or Machine Learning (CS 440 and/or 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 Computer Science or Bioinformatics major(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.
67775	Online	TC3	11:00AM -12:15PM	TR	n.a.	Erickson, J	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Computational Geometry Section Info: Title: Computational Geometry Description: Design and analysis of efficient algorithms for fundamental geometric problems, including convex hulls, Voronoi diagrams, geometric range searching, line segment intersection, polygon triangulation, low-dimensional linear programming, and visibility. Applications of geometric algorithms in computer graphics, mesh generation, geographic information systems, VLSI design, and other areas of computing. A solid background in algorithms (at the level of CS 374) is assumed. Preferred Prerequisite: CS 374 Restriction(s): Restricted to Undergrad - Urbana-Champaign.
67785	Online	TC4	11:00AM -12:15PM	TR	n.a.	Erickson, J	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Computational Geometry Section Info: Title: Computational Geometry Description: Design and analysis of efficient algorithms for fundamental geometric problems, including convex hulls, Voronoi diagrams, geometric range searching, line segment intersection, polygon triangulation, low-dimensional linear programming, and visibility. Applications of geometric algorithms in computer graphics, mesh generation, geographic information systems, VLSI design, and other areas of computing. A solid background in algorithms (at the level of CS 374) is assumed. Preferred Prerequisite: CS 374 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.
58505	Lecture	TO1	3:30PM -4:45PM	TR	Location Pending	Har-Peled, S	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Topics in Algorithms Restriction(s): Restricted to Undergrad - Urbana-Champaign.
58506	Online	TO2	3:30PM -4:45PM	TR	n.a.	Har-Peled, S	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Topics in Algorithms 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.