CS 498

Spring 2018 Part of Term 1

Part of Term 1
Jan 16-May 2

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.

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.
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