CS 498

Fall 2018 All Classes

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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 fall 2018
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
61482	Laboratory	AB1	12:00PM -12:50PM	W	1129 Siebel Center for Comp Sci	Campbell, R Kesan, J Bambenek, J	Part of Term: 1 Date Range: 08/27/18-12/12/18 Section Title: Digital Forensics
61483	Laboratory	AB2	1:00PM -1:50PM	W	1129 Siebel Center for Comp Sci	Campbell, R Kesan, J Bambenek, J	Part of Term: 1 Date Range: 08/27/18-12/12/18 Section Title: Digital Forensics
70418	Laboratory	AB3	2:00PM -2:50PM	W	1129 Siebel Center for Comp Sci	Campbell, R Kesan, J Bambenek, J	Part of Term: 1 Date Range: 08/27/18-12/12/18 Section Title: Digital Forensics
61457	Lecture	AL1	9:30AM -10:45AM	MW	1310 Digital Computer Laboratory	Campbell, R Kesan, J Bambenek, J	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Digital Forensics Section Info: Digital forensics concerns the acquisition and investigation of evidence from all devices capable of storing digital data and is often related to the prosecution of cyber crime and fraud. The class introduces the process of forensic investigation, chain of custody, forensics analysis, court proceedings and the legal justice system. It includes examination of digital storage and network traffic from personal computers, enterprise systems, embedded devices, and mobiles. Laboratory student exercises will use the tools and techniques of digital forensics investigators. Prerequisite: a basic knowledge of computer science concepts including operating systems and networking. Information about pre-requisites and the self-assessment quiz can be seen at this link - http://publish.illinois.edu/digitalforensics1/prerequisite/
71075	Lecture-Discussion	AM1	11:00AM -12:15PM	TR	1404 Siebel Center for Comp Sci	Forsyth, D Walker, T	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Applied Machine Learning
68911	Lecture	AM3	2:00PM -3:20PM	TR	2013 Electrical & Computer Eng Bldg	Miller, A	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Applied Cryptography
68912	Lecture	AM4	2:00PM -3:20PM	TR	2013 Electrical & Computer Eng Bldg	Miller, A	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Applied Cryptography Restriction(s): Restricted to Graduate - Urbana-Champaign.
70185	Lecture-Discussion	AML	3:30PM -4:45PM	TR	1404 Siebel Center for Comp Sci	Forsyth, D Walker, T	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Applied Machine Learning Restriction(s): Restricted to Undergrad - Urbana-Champaign.
43753	Lecture	CD	11:00AM -12:15PM	TR	1103 Siebel Center for Comp Sci	Gunter, C	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Cyber Dystopia Section Info: Section Info: Analyzing the Adverse Impacts of Advances in Computer Technology. The information revolution is bringing changes that are not always seen as positive to the people they affect. Nevertheless there is a strong feeling that the changes it brings are inevitable and that our efforts should be devoted to advancing, enjoying, and profiting from cyber technologies rather than restraining them. But do our efforts in this direction risk the emergence of a cyber dystopia in which many, perhaps most, people are significantly harmed by technology advances? This course focuses on insights into the downsides of this technological progress. We will characterize key aspects of the problem, assess their severity, predict their future, speculate on how much of what we are facing is inevitable, and think about what steps might avoid the most undesirable outcomes. This will be guided by reading and class discussion of recent works on the topic and a project. Learn more from the course web site https://tinyurl.com/cyberdystopia.
70961	Online	CNO	ARRANGED	n.a.	n.a.	Godfrey, P	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Cloud Networking Section Info: Course description: Computer communication networks are among the most important and influential global infrastructures that humanity has created. The goal of this course is to provide a foundational view of communication networks, with a focus on networks enabling modern hyperscale cloud computing. In the first part of this course, we’ll study the principles upon which the Internet and other computer networks are built, and how those principles translate into deployed protocols. In the second part of this course, we build on those principles to learn how to build a network infrastructure that provides the agility to deploy virtual networks on a shared infrastructure, that enables both efficient transfer of big data and low latency communication, and that enables applications to be federated across countries and continents. Topics will include: switching; intradomain routing; the Internet Protocol and interdomain networking; reliability, flow control, congestion control, and their embodiment in TCP; quality of service; network applications; cloud network requirements and traffic patterns; data center network architecture; virtualized and software-defined networks; and wide-area connectivity. The course will involve a significant amount of Unix-based network programming and assumes some familiarity with C or C++. One shorter programming project employs Python. Students will implement realistic network protocols, and gain the perspective of real-world networking challenges through interviews with industry professionals and academic researchers. This course is only for students that are in the Computer Science MCS/MCS-DS Program offered on the Coursera platform. Additional Coursera ID verification and ProctorU fees may apply. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC.
71121	Online	CSF	ARRANGED	n.a.	n.a.	Fagen-Ulmschneider, W	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Accelerated CS Fundamentals Section Info: CS 498 Accelerated Computer Science Fundamentals Accelerated exploration of fundamental concepts in Computer Science including algorithms, data structures, run-time complexity, and object-oriented programming. Includes elementary analysis of arrays, lists, stacks, queues, heaps, binary trees, balanced tress, hash tables, and graphs with a significant component of programming projects. This course requires prior programming experience and is not an introduction to programming. The first unit will familiarize students with the specifics of C++ programming language by building on a student's background in programming. This course will not satisfy any Computer Science degree requirements. It is intended to provide fundamental CS knowledge only. Additional ProctorU fees may apply.
70363	Lecture	DL3	3:30PM -4:45PM	TR	1310 Digital Computer Laboratory	Lazebnik, S	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Introduction to Deep Learning Section Info: 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.
70372	Lecture	DL4	3:30PM -4:45PM	TR	1310 Digital Computer Laboratory	Lazebnik, S	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Introduction to Deep Learning Section Info: 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.
70470	Online	GA	ARRANGED	n.a.	n.a.	Agha, G	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Smart Cities Section Info: CS 498 Smart Cities: Concepts and Technologies The cities of the future will incorporate innovative information technology to optimize water management, power grid, transportation network, communication network, administrative services, and social spaces. The course will provide a technical introduction to relevant computer science concepts and how they are applied to smart cities. Topics covered will include sensor/actuator networks, crowd sourcing, data science, computer security, privacy, and artificial intelligence. Perspectives on potential implications of these technologies for urban living will be also be discussed. Restriction(s): Restricted to O/C Engineering City Scholars students.
66333	Lecture-Discussion	HS3	11:00AM -12:15PM	WF	1310 Digital Computer Laboratory	Sundaram, H	Part of Term: 1 Date Range: 08/27/18-12/12/18 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 new research areas in computational advertising including electronic billboards, moving objects (banners atop taxi cabs) and algorithmic synthesis of personalized advertisements. This class will also discuss issues related to consumer privacy.
66399	Lecture-Discussion	HS4	11:00AM -12:15PM	WF	1310 Digital Computer Laboratory	Sundaram, H	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 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 new research areas in computational advertising including electronic billboards, moving objects (banners atop taxi cabs) and algorithmic synthesis of personalized advertisements. This class will also discuss issues related to consumer privacy. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70198	Lecture-Discussion	KA3	11:00AM -12:15PM	TR	1131 Siebel Center for Comp Sci	Kirlik, A	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Experimental Methods for HCI Section Info: Course description: This course covers conceiving, designing, performing, analyzing data and reporting the results of experiments and usability/UX tests in HCI and empirically evaluating interactive technologies in engineering generally. Topics include defining research questions, selecting experimental objects, tasks, and participants, the ethical protection of subjects, selecting experimental designs, mitigating threats to validity, the collection and analysis of both qualitative and quantitative data, and reporting experimental research in publications. Both parametric and nonparametric data analysis are covered, including the most commonly used inferential statistical tests such as repeated- and independent-measures ANOVA, post-hoc Tukey, Wilcoxon, Mann-Whitney, Kruskal-Wallis and others. Statistical material is taught using methods based on mathematical foundations rather than with statistical software languages or packages in order to provide both a rigorous and intuitive understanding to complement the convenience these programming environments provide in research practice. Grades are based mainly on homework and 2 exams.
70462	Lecture-Discussion	KA4	11:00AM -12:15PM	TR	1131 Siebel Center for Comp Sci	Kirlik, A	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Experimental Methods for HCI Section Info: Course description: This course covers conceiving, designing, performing, analyzing data and reporting the results of experiments and usability/UX tests in HCI and empirically evaluating interactive technologies in engineering generally. Topics include defining research questions, selecting experimental objects, tasks, and participants, the ethical protection of subjects, selecting experimental designs, mitigating threats to validity, the collection and analysis of both qualitative and quantitative data, and reporting experimental research in publications. Both parametric and nonparametric data analysis are covered, including the most commonly used inferential statistical tests such as repeated- and independent-measures ANOVA, post-hoc Tukey, Wilcoxon, Mann-Whitney, Kruskal-Wallis and others. Statistical material is taught using methods based on mathematical foundations rather than with statistical software languages or packages in order to provide both a rigorous and intuitive understanding to complement the convenience these programming environments provide in research practice. Grades are based mainly on homework and 2 exams. Restriction(s): Restricted to Graduate - Urbana-Champaign.
71011	Online	MCO	ARRANGED	n.a.	n.a.	Godfrey, P	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Cloud Networking Section Info: Additional Coursera ID verification and ProctorU fees may apply Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC.
70197	Lecture-Discussion	MV3	12:30PM -1:45PM	TR	1103 Siebel Center for Comp Sci	Viswanathan, M	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Logic Section Info: This course will provide an introduction to mathematical logic from the perspective of computer science, emphasizing decidable fragments of logic and decision algorithms. The topics covered will be motivated by applications in artificial intelligence, databases, formal methods and theoretical computer science. The goal of the course is to prepare students for using logic as a formal tool in computer science. The course will roughly cover the following topics (in this order): syntax, semantics and proof theory of propositional logic, sat-solvers, syntax of first-order, the resolution proof system, syntax of second-order logic, connections between monadic second order logic and regular languages (word and tree, finite and infinite), tree-width and Courcelle's theorem with applications to parametric complexity, finite model theory and descriptive complexity, games and inexpressiveness. Prerequisite: Courses CS 173, and CS 374 or instructor's consent. In particular, students should be familiar with inductive proofs, propositional logic syntax, ability to use quantifiers (forall and exists) to express simple properties in first-order logic, basic properties of finite graphs, simple graph algorithms, finite automata and regular languages, deterministic and non-deterministic computational models, and complexity classes like NP. This section is for either undergraduate or graduate students.
70494	Lecture-Discussion	MV4	12:30PM -1:45PM	TR	1103 Siebel Center for Comp Sci	Viswanathan, M	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Logic Restriction(s): Restricted to Graduate - Urbana-Champaign.
40091	Lecture	VR3	2:00PM -3:15PM	MW	1404 Siebel Center for Comp Sci	Shaffer, E	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Virtual Reality Section Info: Fundamentals of virtual reality systems, including geometric modeling, transformations, graphical rendering, optics, the human vision system, the vestibular system, interface design, human factors, developer recommendations, and technological issues. Implementation exercises and a final project are included. Extensive programming background not required
40092	Lecture-Discussion	VR4	2:00PM -3:15PM	MW	1404 Siebel Center for Comp Sci	Shaffer, E	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Virtual Reality Section Info: Fundamentals of virtual reality systems, including geometric modeling, transformations, graphical rendering, optics, the human vision system, the vestibular system, interface design, human factors, developer recommendations, and technological issues. Implementation exercises and a final project are included. Extensive programming background not required Restriction(s): Restricted to Graduate - Urbana-Champaign.
67900	Lecture	WN3	9:30AM -10:45AM	TR	1302 Siebel Center for Comp Sci	Kravets, R	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Section Title: Wireless Network Lab Section Info: Wireless networks are everywhere in our world, one laptops, smartphones, sensor and the new IoT devices popping up everywhere. Understanding how wireless networks work and why they break is the key to their successful deployment and integration. In the first half of this class, we focus on the basics of wireless networking, from the physical transmission of radio signals to the impact of lossy communication on higher layer routing and transport protocols. The second half of the class is dedicated to student let topics, including sensor networks, IoT, security and privacy, energy conservation and general performance improving techniques. Over the course of the semester, students design and implement a group project using a variety of wireless devices and technologies, ending with a project report and a poster presentation of their work.
67901	Lecture	WN4	9:30AM -10:45AM	TR	1302 Siebel Center for Comp Sci	Kravets, R	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Wireless Network Lab Section Info: Wireless networks are everywhere in our world, one laptops, smartphones, sensor and the new IoT devices popping up everywhere. Understanding how wireless networks work and why they break is the key to their successful deployment and integration. In the first half of this class, we focus on the basics of wireless networking, from the physical transmission of radio signals to the impact of lossy communication on higher layer routing and transport protocols. The second half of the class is dedicated to student let topics, including sensor networks, IoT, security and privacy, energy conservation and general performance improving techniques. Over the course of the semester, students design and implement a group project using a variety of wireless devices and technologies, ending with a project report and a poster presentation of their work. Restriction(s): Restricted to Graduate - Urbana-Champaign.