CS 498

Spring 2020 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 spring 2020
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
66289	Lecture	AM1	12:30PM -1:45PM	WF	Digital Computer Laboratory	Forsyth, D	Part of Term: 1 Date Range: 01/21/20-05/06/20 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. Prereq: A course in probability or statistics, a course in linear algebra, and some programming experience. Restriction(s): Restricted to Graduate - Urbana-Champaign.
65685	Lecture	AML	12:30PM -1:45PM	WF	Digital Computer Laboratory	Forsyth, D	Part of Term: 1 Date Range: 01/21/20-05/06/20 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
68121	Discussion/ Recitation	DSG	12:30PM -1:50PM	MW	Everitt Laboratory	Han, J Iyer, R	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Data Science & Analytics Restriction(s): Restricted to Graduate - Urbana-Champaign.
65868	Online	DSO	ARRANGED	n.a.	n.a.	Farivar, R	Part of Term: 1 Date Range: 01/21/20-05/06/20 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	Discussion/ Recitation	DSU	12:30PM -1:50PM	MW	Everitt Laboratory	Han, J Iyer, R	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 3 hours Section Title: Data Science & Analytics
55128	Lecture-Discussion	IR1	9:30AM -10:45AM	TR	Siebel Center for Comp Sci	Hauser, K	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 3 hours Section Title: Intelligent Robots 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.
55129	Lecture-Discussion	IR2	9:30AM -10:45AM	TR	Siebel Center for Comp Sci	Hauser, K	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Intelligent Robots 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. Restriction(s): Restricted to Graduate - Urbana-Champaign.
69725	Online	ITO	ARRANGED	n.a.	n.a.	Caesar, M	Part of Term: 1 Date Range: 01/21/20-05/06/20 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	Lecture	LB1	3:30PM -4:45PM	WF	Siebel Center for Comp Sci	Li, B	Part of Term: 1 Date Range: 01/21/20-05/06/20 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.
47220	Lecture	LB2	3:30PM -4:45PM	WF	Siebel Center for Comp Sci	Li, B	Part of Term: 1 Date Range: 01/21/20-05/06/20 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. Restriction(s): Restricted to Graduate - Urbana-Champaign.
39660	Lecture	PS3	12:30PM -1:45PM	TR	Siebel Center for Comp Sci	Smaragdis, P	Part of Term: 1 Date Range: 01/21/20-05/06/20 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: 01/21/20-05/06/20 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.
61851	Lecture	RK1	2:00PM -3:15PM	WF	Digital Computer Laboratory	Angrave, L Arsan, D	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 3 hours Section Title: Art and Science of Web Prog Section Info: Presents client- and server-side technologies that enable modern Web applications. Topics include the building blocks of the Web (HTML, CSS, the Document Object Model, Javascript) and data exchange (HTTP, JSON, RESTful APIs, and SQL/NoSQL databases). Programming assignments will expose students to CSS preprocessors, grid systems, and full-stack Javascript frameworks that scaffold development and testing. In addition, students will work in teams to design, implement and deploy a full-featured web application. Prerequisites: CS225.
61850	Lecture	RK2	2:00PM -3:15PM	WF	Digital Computer Laboratory	Angrave, L Arsan, D	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Art and Science of Web Prog Section Info: Presents client- and server-side technologies that enable modern Web applications. Topics include the building blocks of the Web (HTML, CSS, the Document Object Model, Javascript) and data exchange (HTTP, JSON, RESTful APIs, and SQL/NoSQL databases). Programming assignments will expose students to CSS preprocessors, grid systems, and full-stack Javascript frameworks that scaffold development and testing. In addition, students will work in teams to design, implement and deploy a full-featured web application. Prerequisites: CS225. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70960	Online	VR1	ARRANGED	n.a.	n.a.	Shaffer, E	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 3 hours Section Title: Virtual Reality for CSP Section Info: Virtual Reality, restricted to City Scholars Program. Restriction(s): Restricted to O/C Engineering City Scholars students.
50232	Lecture	VR3	9:30AM -10:45AM	TR	Digital Computer Laboratory	Shaffer, E	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 3 hours Section Title: Virtual Reality
50234	Lecture	VR4	9:30AM -10:45AM	TR	Digital Computer Laboratory	Shaffer, E	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Virtual Reality Restriction(s): Restricted to Graduate - Urbana-Champaign.