CS 598

Spring 2023 All Classes

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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 2023
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
48269	Lecture	BAN	11:00AM -12:15PM	TR	1302 Siebel Center for Comp Sci	Banerjee, A	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Deep Generative & Dyn. Models Section Info: Recent years have seen considerable advances in generative models, which learn distributions from data and also generate new data instances from the learned distribution; and dynamical models, which model systems with a dynamical or temporal component. Both of these developments have been leveraging advances in deep learning. The course will cover key advances in generative and dynamical models, including variational auto-encoders, normalizing flows, generative adversarial networks, diffusion models, neural differential equations, learning operators, among other topics. Prereq -- CS 446 or equivalent Old course website, from Fall'21 -- https://arindam.cs.illinois.edu/courses/f21cs598/ 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. Not intended for First Time Freshman students.
46412	Online	BOF	11:00AM -12:15PM	R	n.a.	Banerjee, A	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Deep Gen & Dyn Models Section Info: This is an online overflow section for CS 598 BAN. Restriction(s): Restricted to Graduate - Urbana-Champaign.
46410	Lecture-Discussion	CDS	9:30AM -10:45AM	MW	1302 Siebel Center for Comp Sci	Alagappan, R	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Cloud Storage Systems Section Info: Cloud data centers are powered by storage systems such as key-value stores, file systems, and databases. This course will explore such storage systems, focusing on their theoretical foundations and practical aspects. First, we will learn about data-structural ideas (e.g., LSMs, Be-Trees) and how they have led to the construction of efficient storage systems. Then, we will focus on practical systems issues (e.g., data safety, crash recovery) in building these systems. This course will be research-oriented and discussion-based: most classes will be based on research papers. An essential part of this course is a final research project. At the end of the course, students will be able to critique systems research papers, understand fundamental problems in storage systems, and have experience working on a research project. Students must have a background in undergraduate-level operating systems (CS 423). Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
39667	Online	CMC	11:00AM -12:15PM	TR	n.a.	Jabbarvand Behrouz, R	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: ML for Software Engineering Section Info: This section is for MCS Chicago students only. The section will utilize online and in-person meetings, as arranged by the instructor. Please note that the course is synchronous. 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 Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS: Computer Sci OFF - UIUC.
39665	Lecture-Discussion	DH	11:00AM -12:15PM	TR	1214 Siebel Center for Comp Sci	Heath, D	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Secure Computation Section Info: This course covers secure multiparty computation (MPC), a suprisingly powerful branch of cryptography that allows mutually untrusting parties to work together to securely run programs on private data. We will discuss both the theory and the practice of this emerging technology. Our discussion will cover computing on encrypted data, zero-knowledge proofs, oblivious RAM, and more. Suggested Prerequisites: CS 374, CS 361. Mathematical maturity is required; participants will be expected to read and write formal definitions/proofs, and to read and present research papers. Basic background on probability theory is recommended. Formal cryptography background is explicitly not required. 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. Not intended for First Time Freshman students.
43732	Online	DLH	ARRANGED	n.a.	n.a.	Sun, J	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Deep Lrng for Healthcare Section Info: This course covers deep learning (DL) methods, healthcare data, and applications using DL methods. For this semester, we will emphasize deep learning for drug discovery and development use cases. The students will be expected to review book chapters and answer homework assignments related to ML for drug discovery and development and other healthcare applications. 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 is only for students that are in the Computer Science MCS-DS Program. Additional ProctorU fees may apply. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC.
46428	Lecture-Discussion	FTS	2:00PM -3:15PM	TR	1302 Siebel Center for Comp Sci	Ganesan, A	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Fault-Tol. Consisten Data Sys Section Info: How are distributed systems built in the modern data center? How do hardware trends impact system design? How do we rethink decades-old protocols and ideas for the modern data center? If you are curious about answers to these questions, this course is for you. This course will dive deep into replication and consensus protocols, geo-replicated systems, distributed transactions, and various consistency models and how to implement them. We will also learn how traditional distributed protocols have been rearchitected for emerging hardware such as persistent memory, RDMA, and programmable switches and NICs. We will also discuss case studies from production systems. Pre-requisites: Operating Systems (CS 423) or Distributed Systems (CS 425) 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. Not intended for First Time Freshman students.
60197	Lecture-Discussion	HAZ	12:30PM -1:45PM	WF	0216 Siebel Center for Comp Sci	Zhao, H	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Transfer Learning Section Info: 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. Not intended for First Time Freshman students.
40235	Online	HOF	12:30PM -1:45PM	WF	n.a.	Zhao, H	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Transfer Learning Section Info: This is an online overflow section for CS 598 HAZ. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
43810	Lecture-Discussion	JBR	11:00AM -12:15PM	TR	1304 Siebel Center for Comp Sci	Jabbarvand Behrouz, R	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: ML for Software Engineering Section Info: For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister. 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 First Time Freshman students.
43808	Lecture-Discussion	JGE	11:00AM -12:15PM	WF	2200 Sidney Lu Mech Engr Bldg	Erickson, J	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Algorithms for 1D Structures Section Info: Algorithms for 1D Structures- This course will be a broad introduction to algorithms for curves and graphs embedded in the plane or other surfaces. Algorithmic questions about curves have been a driving force in topology since its inception more than a century ago. Planar and near-planar graphs have long been fertile ground for algorithms research, both because they naturally model many classes of networks that arise in practice, and because they admit simpler and faster algorithms than more general graphs. There is a rich interplay between these two domains, drawing on a common pool of techniques from geometry, topology, and combinatorics. Potential topics include topological graph theory; homotopy, homology, and other topological invariants; specialized algorithms for shortest paths, maximum flows, and minimum cuts; efficient approximation schemes for NP-hard problems; and applications in VLSI design, computer graphics, computer vision, motion planning, geographic information systems, and other areas of computing. Specific topics will depend on the interest and expertise of the students. Students in all areas of computer science, mathematics, and related disciplines are welcome. CS 473 and/or Math 525 are recommended as prerequisites, but not required; necessary background material will be introduced as needed. 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. Not intended for First Time Freshman students.
62819	Online	LHO	ARRANGED	n.a.	n.a.	Sun, J	Part of Term: 1 Date Range: 01/17/23-05/03/23 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. For this semester, we will emphasize deep learning for drug discovery and development use cases. The students will be expected to review book chapters and answer homework assignments related to ML for drug discovery and development and other healthcare applications. 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. Section LHO is not intended for online MCS students. This is the on-campus section. This course is only for students that are in the Computer Science MCS-DS Program. Additional ProctorU fees may apply. Restriction(s): Not intended for MCS:Computer Sci Online -UIUC.
39668	Lecture	MAF	2:00PM -3:15PM	TR	1310 Digital Computer Laboratory	Forbes, M	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Algeb & Geomet Complex Theory Section Info: The use of algebraic techniques (addition, multiplication, derivatives, and beyond) is pervasive in the design of efficient computation. This course will explore how these techniques apply to problems of an inherently algebraic nature, such as computing the determinant of a matrix, as well as for tasks that seemingly lack algebraic structure, such as combinatorial optimization. The course will develop the theory of using algebraic circuits to compute multivariate polynomials. This includes non-trivial algorithmic paradigms for efficient computation in this model (upper bounds), methods for proving that certain polynomials cannot be efficiently computed (lower bounds), as well as algorithms for efficiently deciding whether a given algebraic circuit computes the zero polynomial (polynomial identity testing). The course will also explore the underlying algebraic geometry of algebraic computation, especially in order to address foundational questions. This exploration will be with an eye toward the Geometric Complexity Theory program of Mulmuley and Sohoni, which aims to resolve the algebraic version of the P vs NP question. 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. Not intended for First Time Freshman students.
31665	Lecture-Discussion	OSS	3:30PM -4:45PM	TR	2233 Everitt Laboratory	Bates, A	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Operating System Security Section Info: For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister. 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. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
46413	Lecture-Discussion	TH1	11:00AM -12:15PM	WF	3217 Everitt Laboratory	Mehta, R	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Recent Advances in TCS Section Info: The course will aim to cover exciting recent advances within TCS from optimization, algorithmic game theory, computational geometry, and complexity. It will be a seminar style course where students will be asked to present papers and work on research projects in groups. Pre-requisite: CS 473. Also, it would help to have done some advanced theory courses like approximation/randomized algorithms. 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. Not intended for First Time Freshman students.