CS 598

Spring 2021 Part of Term 1

Part of Term 1
Jan 25-May 5

Special Topics

SHOW FINAL EXAM SCHEDULE

Credit: 2 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.

May be repeated in the same or separate terms if topics vary.

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CS 598 class schedule data for spring 2021
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
39669	Online	ACV	9:30AM -10:45AM	MW	n.a.	Wang, Y	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Advanced Computer Vision Section Info: Title: Advanced Computer Vision Descriptive paragraph: This course will cover advanced research topics in computer vision, with emphasis on recognition tasks and deep learning. Building on the introductory materials in CS 543 (Computer Vision), this course will prepare graduate students in both the theoretical foundations of computer vision and the state-of-the-art approaches to building real-world computer vision systems. We will investigate data sources, model architectures, and learning algorithms that are useful for understanding and manipulating visual data. This course will start by focusing on representation and reasoning for large amounts of data (images, videos, 3D point clouds, associated tags, text, gps-locations, etc). We will then in particular discuss recent efforts towards visual learning and reasoning with less human supervision. Students will be required to read, present, critique, and discuss research papers and perform a related research project. By the end of the course, students will be able to understand and implement the state-of-the-art algorithms as well as identify important open questions and future research directions. Students will be also ready to conduct research in computer vision and its relevant domains such as robotics. It is strongly advised that you have CS 543 or an equivalent computer vision course or permission of the instructor is required. CS 446 or CS 498 DL is recommended but not 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.
43773	Online	AIG	11:00AM -12:15PM	MW	n.a.	Forsyth, D	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: AI & Comp Vis mt Comp Graphics Section Info: CS 598: AI and computer vision meet computer graphics This course will look at the impact of recent vision and learning methods on rendering, geometry, animation and computational photography. Topics include: basic rendering, differentiable and neural rendering, intrinsic images and BRDF estimation, image transformations, relighting images, predicting novel views, colorization, style transfer, geometry estimation and deformation; motion estimation and transfer for animation, and facial animation. Students will be expected to: read and present recent papers; implement current methods successfully; and offer a final project. This course is mostly suitable for graduate students. It is strongly suggested that students are fluent programming skills; at least one course in machine learning; some experience in computer graphics. 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.
40099	Online	CAL	9:30AM -10:45AM	WF	n.a.	Ren, L	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Consensus Algorithms Section Info: This course covers classic results and recent advances in consensus algorithms. The course studies different problem formulations of consensus including Byzantine agreement, broadcast primitives and state machine replication (a.k.a, blockchains); different models and assumptions regarding timing, fault pattern, cryptography, and setup; state-of-art algorithms and lower bounds under various combinations of settings; common algorithm design techniques including randomization, leader-based approach, and quorum systems; Nakamoto’s new permissionless model, longest-chain paradigm, the Bitcoin protocol, its mathematical analysis, proposals to improve Nakamoto consensus; longest-chain protocols with other resources or stake, and their connection to classic consensus algorithms; and recent advances in consensus algorithms inspired by Nakamoto's paradigm. 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.
44818	Online	CG	11:00AM -12:15PM	TR	n.a.	Gunter, C	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Sec. & Privacy for Home IOT Section Info: Title: CS598CG Security and Privacy for Home IoT Description: The Internet of Things (IoT) for homes describes an increasing deployment of networked devices located in homes and able to collect sensor inputs, communicate via local networks and the internet, and actuate events within the home. Examples include networked thermostats, fire alarms, security systems, and home health monitoring systems. They also include smart speakers and appliances. The course will explore the security and privacy ramifications of such devices covering both issues with the devices themselves and their broader ecosystem of smartphones and cloud systems. Format: This class will meet synchronously via two 75 minute online meetings each week. Students will be expected to read, present, and discuss advanced research papers on these topics and carry out a final project. 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.
60198	Online	DEL	1:00PM -2:15PM	TR	n.a.	Delgosha, P	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Methods & Algor. in Lg. Graphs Section Info: Title: Mathematical Methods and Algorithms in Large Graphs Description: Many modern data arrives in a form that is best represented by combinatorial structures such as graphs, rather than classical time series. Graphs show up in various examples and applications, ranging from social networks and internet graphs to biological data. Modeling the interaction between objects as a graph allows us to better understand, analyze, and predict the behavior of such networks. Such understanding is crucial in subsequent applications, including but not limited to estimation, learning, data compression, and community detection. The focus of this course is to study mathematical tools to analyze graphs, specifically random graphs as models for large graphical data. We further employ this analysis to discuss several applications such as learning and compression. This is a graduate level course which is open to graduate students with a good level of mathematical maturity and a strong background in probability, as well as some basic background in graph theory. In this course, the students will develop skills in evaluating research papers and are expected to conduct a final research project. The class will be delivered online synchronously. 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.
58502	Online	DK	3:30PM -4:45PM	WF	n.a.	Khurana, D	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Cryptography Section Info: Cryptography, that started as the study of secret communication, has undergone a major revolution in the last few years. It now helps us realize a variety of seemingly impossible tasks: from allowing computations on secret data without revealing the data itself, to offloading computation to untrusted clients while maintaining verifiable results, and even making programs unintelligible while preserving functionality. This course will cover a selection of such cutting-edge topics in modern cryptography. We will understand how an adversary that breaks advanced protocols can be transformed into an adversary that contradicts a basic assumption such as the hardness of factoring. Our focus will be on understanding key ideas in cryptography research published over the last few years, and identifying new directions and problems for the future. This is a seminar course in Cryptography. Students will be expected to read and discuss research papers in the second part of the course. 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.
65866	Online	DSO	ARRANGED	n.a.	n.a.	Park, T	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Advanced Bayesian Modeling Section Info: This course is only for students that are in the Computer Science Online MCS Program. ProctorU fees may apply. Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
43806	Online	EWS	3:30PM -4:45PM	TR	n.a.	Chandrasekharan, E	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Social Computing Section Info: Description: In this course, we will explore how social behaviors are mediated by computational systems. Focusing on recent advances in HCI, NLP, and human-centered AI, we will understand, build, and evaluate social computing systems. Through this course, students will read and critique high-impact research papers, lead class discussions, provide and receive constructive peer-feedback, engage with guest speakers, and execute a new research idea for their final paper. 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.
43810	Online	JBR	9:30AM -10:45AM	TR	n.a.	Jabbarvand Behrouz, R	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: ML for Software Engineering Section Info: Objectives: The purpose of this course is to help students explore and understand the applications of machine learning to solve real-world software engineering problems. Students will become familiar and obtain knowledge about (1) fundamentals and advanced topics in software engineering as well as (2) how machine learning and data mining techniques can be used at different stages of software development to ensure quality and reliability of software Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for NDEG:Computer Science Onl-UIUC, MCS:Computer Sci Online -UIUC, or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
60197	Online	LCE	11:00AM -12:15PM	TR	n.a.	Adve, V	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Lang & Compilers for Edge Comp Section Info: This course will be a literature review of recent papers on compilers and optimization techniques for resource-constrained heterogeneous parallel systems used in edge computing. Such systems increasingly use specialized, domain-specific accelerators to deliver the necessary performance and energy efficiency under tight resource constraints. We will begin with a brief discussion of example domains important in edge computing applications, e.g., computer vision, image processing, speech processing, SLAM, and autonomous control systems. The course will then cover relevant papers in the areas of compilers, optimization techniques, and accuracy-performance-energy tradeoffs for such systems. 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.
62819	Online	LHO	ARRANGED	n.a.	n.a.	Sun, J	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Deep Learning for Healthcare Section Info: This course covers deep learning (DL) methods, healthcare data, and applications using DL methods. The courses include activities such as video lectures, self-guided programming labs, homework assignments (both written and programming), and a large project. You are expected to learn deep learning models such as deep neural networks, convolutional neural networks, recurrent neural networks, autoencoder, attention models, graph neural networks and deep generative learning. You will also get a chance to learn different healthcare applications using DL methods such as clinical predictive models, computational phenotyping, patient risk stratification, treatment recommendation, clinical natural language processing, and medical imaging analysis. Besides learning DL algorithms, the course will focus on hands-on experiences for data scientists and machine learning engineers to implement various practical healthcare models on diverse medical data. You will learn popular deep learning frameworks like PyTorch, and data science software like Jupyter Notebook. Basic machine learning will be helpful but not strictly required. You should have good programming skills in Python and good understanding in linear algebra and calculus. You should also have sufficient system knowledge such as using Linux and setting up programming environments on the cloud. This course will be taught on the Coursera platform, and will include in-class meetings as scheduled for additional discussion time. This section will have one or more proctored online exams. Proctoring options may include fee-based ProctorU and approved testing facilities that carry no fees. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
69011	Online	MEB	2:00PM -3:15PM	TR	n.a.	El-Kebir, M	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Computational Cancer Genomics Section Info: Title: Computational cancer genomics This course focuses on recent algorithmic methods in cancer genomics, including somatic variant calling, phylogeny inference and identification of driver mutations. Students will study the underlying principles of these methods and the application of these methods to cancer genomics data. This course is appropriate for graduate students in computer science, bioengineering, mathematics and statistics. Familiarity with basic statistics, probability and algorithms is expected. 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.
39670	Online	OOK	2:00PM -3:15PM	TR	n.a.	Koyejo, O	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Probabilistic Graphical Models 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.
31665	Online	OSS	3:30PM -4:45PM	TR	n.a.	Bates, A	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Operating System Security Section Info: Description: This course provides an in-depth examination of issues in operating system security, and assumes prior knowledge of fundamental security concepts. We will be studying research in securing computer and operating systems, with a focus on the design of authorization systems and a thorough examination of concepts, past and present, that continue to be influential in secure systems design. Topics will include protection systems, foundational security principles, classic approaches to system security, system vulnerabilities, mandatory access controls in research and commercial operating systems, capability systems, virtual machines, and security kernels. Selected seminal and current papers in the field will also aid in providing context and further understanding of the area. Meets: This class will be 100% online and synchronous. There will be an asynchronous option for students that are unable to attend class at the scheduled time (e.g., local time zone is European / Asian, low-speed Internet access). 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.
40235	Online	PSO	ARRANGED	n.a.	n.a.	Liang, F	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Practical Statistical Learning Section Info: For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration All content will be delivered online in an asynchronous manner. There will be no synchronous class meetings. 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. Restricted to NDEG:Computer Science Onl-UIUC, MCS:Computer Sci Online -UIUC, or MCS:Computer Sci Online -UIUC.
31662	Online	PSR	3:30PM -4:45PM	TR	n.a.	Forbes, M	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Pseudorandomness Section Info: Topic: Decision Under Uncertainty 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.
69012	Online	RK	12:00PM -1:15PM	TR	n.a.	Kumar, R	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: HCI for ML Section Info: Explores the use of data-driven methods to support creative design processes by examining recent work in human computer-interaction, product design, cognitive science, machine learning, graphics, vision, and natural language processing. Students will read and discuss recent papers from these fields, and work in teams on a multi-week project to build data-driven tools to solve real-world design problems. Practical data mining and machine learning knowledge is emphasized: crowdsourcing and web scraping, model and feature selection, parameter tuning. The course has no formal prerequisites, but students should be algorithmically and programmatically mature. 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.
48261	Online	SVA	3:30PM -4:45PM	TR	n.a.	Adve, S Gunter, E	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: App-Cust Heterogeneous Systems Section Info: Application-Customized Heterogeneous Systems Hardware design is evolving towards integrating multiple accelerators (IP components) to obtain application-customized systems. These components will likely be connected with a deep communication hierarchy spanning components on a single chip to within the cloud. Design methods that allow seamless integration of such components will be critical to sustainably achieving cost and performance goals for new applications. This course will cover the hardware and software challenges and application drivers of such heterogeneous system design. Topics will include accelerator architectures, heterogeneous memory and communication systems, scheduling, programming (e.g., domain specific languages and frameworks), the hardware-software interface (e.g., virtual instruction sets), and requirements of several application domains (e.g., virtual reality, machine learning, robotics, graph analytics, and human-centric computing). Students will be required to present and critique research papers and perform a substantial team project. Pre-requisites: CS 433 or equivalent or permission of the instructor. 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.
46410	Online	WSI	9:30AM -10:45AM	TR	n.a.	Vasisht, D	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 4 hours Section Title: Wireless Sys for The IOT Section Info: This graduate-level seminar will cover the latest research in the Internet of Things from a networked systems perspective. We will discuss advances in foundational networking and systems design techniques for the Internet of Things and highlight novel inference approaches to connect the underlying data to its end applications. Students will experience and possibly build applications on top of novel IoT platforms in digital healthcare, data-driven agriculture, ocean sensing, autonomous vehicles, security, satellites, and others. 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.