CS 498

Spring 2018 Part of Term 1

Part of Term 1
Jan 16-May 2

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 2018
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
61698	Laboratory	AB1	9:00AM -10:50AM	F	Siebel Center for Comp Sci	Bambenek, J	Part of Term: 1 Date Range: 01/16/18-05/02/18 Section Title: Digital Forensics II Section Info: This lab section will meet in 0222 Siebel Center
65904	Laboratory	AB2	11:00AM -12:50PM	F	Siebel Center for Comp Sci	Bambenek, J	Part of Term: 1 Date Range: 01/16/18-05/02/18 Section Title: Digital Forensics II
61697	Lecture	AL1	9:00AM -9:50AM	MW	Siebel Center for Comp Sci	Bambenek, J	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Digital Forensics II Section Info: This is a course for graduate students and advanced undergraduates wanting to develop greater depth and breadth in digital forensics and assumes a basic knowledge of the material covered in Digital Forensics I. Topics include standards of evidence, investigatory procedures, forms of investigation, legal procedures, reasoning about evidence, psychology of cyber crime, anti-forensics, multimedia forensics, computer forensics, web browser forensics, embedded systems forensics, network forensics, cloud forensics, applications forensics, and fraud examination. It introduces known barriers and open challenges in the field. Prerequisite: Completion of Digital Forensics I or special permission granted by the instructor.
65685	Lecture	AML	3:30PM -4:45PM	TR	Digital Computer Laboratory	Forsyth, D	Part of Term: 1 Date Range: 01/16/18-05/02/18 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. Prereq: A course in probability or statistics, a course in linear algebra, and some programming experience
67942	Online	AMO	ARRANGED	n.a.	n.a.	Forsyth, D	Part of Term: 1 Date Range: 01/16/18-05/02/18 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.
67780	Lecture-Discussion	CPS	ARRANGED	n.a.	Illini Center	Caccamo, M	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 3 hours Section Title: Cyber Physical-Systems Section Info: In this course, we will delve into topics that deal with the design and temporal analysis of cyber-physical and embedded systems. The goal of this course is to provide a deep understanding about resource management, analysis and safety of modern embedded systems that interact with the physical world, especially those that have different degrees of criticality and stringent timing requirements. Examples of such systems include modern automobiles, avionics and flight systems, space vehicles and satellites, medical equipment, power distribution grid, and robotics devices among others. This course has a mixed structure with both regular lectures and some research paper presentations. Students will give one in class presentation about state-of-art research papers published in top conferences and journals. The course is structured to improve students' ability for critical thinking. In-class discussion will focus on classic real-time systems theory and some state-of-art research work on cyber-physical and real-time embedded systems. Part of studied theory will be applied to the design of a simple control system for an Unmanned (Aerial) Vehicle. Course requirements include a project to be completed by students organized as teams. Prerequisites: This class admits both senior undergrads and graduate students. The prerequisite for this class is CS241 (System Programming), or consent of the instructor. Restriction(s): Restricted to O/C Engineering City Scholars students.
68232	Online	CSP	ARRANGED	n.a.	n.a.	Forsyth, D	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 3 hours Section Title: Applied Machine Learning Restriction(s): Restricted to O/C Engineering City Scholars students.
68121	Lecture	DSG	10:00AM -11:30AM	MW	Siebel Center for Comp Sci	Campbell, R Iyer, R	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Data Science & Analytics
65868	Online	DSO	ARRANGED	n.a.	n.a.	Campbell, R Farivar, R	Part of Term: 1 Date Range: 01/16/18-05/02/18 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 Coursera ID verification and ProctorU fees may apply. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC or NDEG:Computer Science Onl-UIUC.
68120	Lecture	DSU	10:00AM -11:30AM	MW	Siebel Center for Comp Sci	Campbell, R Iyer, R	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 3 hours Section Title: Data Science & Analytics
67978	Lecture-Discussion	GH3	10:30AM -11:50AM	MW	Transportation Building	Herman, G	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 3 hours Section Title: Learning and Computer Science Section Info: Title: Learning and Computer Science Topic: Learning how to program is increasingly becoming a fundamental skill for many disciplines, and the teaching of programming has been actively studied for more than 50 years. Through reading the literature and interactive discussions, the course will provide an overview of what we know (and what we don’t know!) about how people learn, in general and in computer science specifically. Topics include organizing knowledge, creating concepts, interacting with visual displays, and managing cognitive load. Students will also learn how to design and perform educational research studies with the course culminating in students writing the core of a National Science Foundation grant proposal. Prerequisites: CS 225 or consent of Instructor. Restriction(s): Restricted to Engineering. Restricted to Undergrad - Urbana-Champaign.
67979	Lecture-Discussion	GH4	10:30AM -11:50AM	MW	Transportation Building	Herman, G	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Learning and Computer Science Section Info: Title: Learning and Computer Science Topic: Learning how to program is increasingly becoming a fundamental skill for many disciplines, and the teaching of programming has been actively studied for more than 50 years. Through reading the literature and interactive discussions, the course will provide an overview of what we know (and what we don’t know!) about how people learn, in general and in computer science specifically. Topics include organizing knowledge, creating concepts, interacting with visual displays, and managing cognitive load. Students will also learn how to design and perform educational research studies with the course culminating in students writing the core of a National Science Foundation grant proposal. Prerequisites: CS 225 or consent of Instructor. Restriction(s): Restricted to Graduate - Urbana-Champaign.
55504	Lecture	MS3	10:00AM -11:20AM	W	Siebel Center for Comp Sci	Snir, M	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 3 hours Section Title: Parallel Algorithms Section Info: Topic: The course will survey the algorithmic foundations of parallel computing. This includes basic parallel programming models; operation and communication complexity; basic parallel algorithms for linear algebra, FFT, sorting and graph algorithms; networks and routing. Restrictions: Restricted to Computer Science major(s). Restricted to students with Senior or Graduate class standing. Prerequisite: CS 374
55505	Lecture	MS4	10:00AM -11:20AM	W	Siebel Center for Comp Sci	Snir, M	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Parallel Algorithms Section Info: Topic: The course will survey the algorithmic foundations of parallel computing. This includes basic parallel programming models; operation and communication complexity; basic parallel algorithms for linear algebra, FFT, sorting and graph algorithms; networks and routing. Restrictions: Restricted to Computer Science major(s). Restricted to students with Senior or Graduate class standing. Prerequisite: CS 374 Restriction(s): Restricted to Graduate - Urbana-Champaign.
65864	Online	ONL	ARRANGED	n.a.	n.a.	Forsyth, D Chen, B	Part of Term: 1 Date Range: 01/16/18-05/02/18 Section Title: Applied Machine Learning Section Info: Restricted to online non-degree, online MCS, online MSAE, online MSME, and online MSCE students. For more details on this course section, please see http://engineering.illinois.edu/online/courses/. Restriction(s): Restricted to MS: Civil Engr - Online - UIUC, MCS:Computer Sci Online -UIUC, MS:Mechanical Engineerng -UIUC, MS: Aerospace Engr-Online-UIUC, NDEG:Grad Nondegree-CE-UIUC, NDEG:Undergrad Nondeg-CE-UIUC, or MENG:Mech Engineering Onl-UIUC.
39660	Lecture	PS3	12:30PM -1:45PM	TR	Siebel Center for Comp Sci	Smaragdis, P	Part of Term: 1 Date Range: 01/16/18-05/02/18 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.
67074	Lecture	PS4	12:30PM -1:45PM	TR	Siebel Center for Comp Sci	Smaragdis, P	Part of Term: 1 Date Range: 01/16/18-05/02/18 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. Restriction(s): Restricted to Graduate - Urbana-Champaign.
67775	Lecture-Discussion	TC3	2:00PM -3:15PM	WF	Siebel Center for Comp Sci	Chan, T	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 3 hours Section Title: Computational Geometry
67785	Lecture-Discussion	TC4	2:00PM -3:15PM	WF	Siebel Center for Comp Sci	Chan, T	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Computational Geometry Restriction(s): Restricted to Graduate - Urbana-Champaign.
50232	Lecture	VR3	4:00PM -5:15PM	MW	Digital Computer Laboratory	Angrave, L	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 3 hours Section Title: Virtual Reality
50234	Lecture	VR4	4:00PM -5:15PM	MW	Digital Computer Laboratory	Angrave, L	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Virtual Reality Restriction(s): Restricted to Graduate - Urbana-Champaign.