CS 598

Fall 2024 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 fall 2024
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
67237	Lecture-Discussion	AB	12:30PM -1:45PM	TR	2233 Everitt Laboratory	Bates, A	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Endpt Threat Detect. & Invest. Section Info: This course provides an in-depth examination of how attackers are audited, detected, and investigated on endpoint systems. Auditing is a foundational concept in operating system security, but has only recently come into its own as an area of active study. We will be studying research, both past and present, on the design of audit frameworks that permit the detection of security violations. Topics will include event logging in commodity operating systems, data provenance analysis, threat investigation, and threat detection. Selected seminal and current papers in the field will also aid in providing context and further understanding of the area. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
46989	Lecture-Discussion	AIE	12:30PM -1:45PM	WF	1302 Siebel Center for Comp Sci	Zhang, M	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: AI Efficiency: Sys. & Algor. Section Info: Topic: AI Efficiency: Systems & Algorithms Are you curious about how system techniques enable today's large-scale model training and deliver ultra-fast inference? Do you have a passion for making AI accessible to all by using advanced system and algorithm techniques, thereby significantly reducing the cost of training and deploying deep learning models? If so, this course is for you. The course provides an in-depth view of AI efficiency, focusing on the core concepts of both AI systems and algorithmic methods. We will explore and discuss seminal works in the field of AI systems, such as ZeRO-style data parallelism, tensor parallelism, pipeline parallelism, sequence parallelism, and 3D parallelism. We will also go over inference optimization techniques, such as FlashAttention, blocked KV cache, speculative decoding, and various compression algorithms. Students will have the opportunity to present existing works in the field of AI efficiency and learn to write paper reviews, which help develop critical thinking skills. Students will also work on group projects, which involve the design, hands-on implementation, and evaluation of AI systems or algorithms. The group project will provide students with valuable experience in working with real AI systems, and a deeper understanding of the complexities involved in optimizing AI efficiency. 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.
69375	Online	AO2	ARRANGED	n.a.	n.a.	Willis, C	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Foundations of Data Curation Section Info: This section is only for students that are in the Computer Science Online MCS/MCS-DS Program offered on the Coursera platform. Additional ProctorU fees may apply. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
49221	Online	CC1	ARRANGED	n.a.	n.a.	Farivar, R	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Cloud Computing Capstone Section Info: This section is only for students that are in the Computer Science Online MCS/MCS-DS Program offered on the Coursera platform. Additional ProctorU fees may apply. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC.
63589	Lecture-Discussion	CSC	11:00AM -12:15PM	TR	0216 Siebel Center for Comp Sci	Chekuri, C	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Topics in Graph Algorithms Section Info: The field of graph algorithms has seen a number of fundamental advances in the last few years including much faster algorithms for classical problems such as shortest paths, flows, and cuts. A diverse set of ideas have contributed to these developments. The course will cover some of these new results and the relevant technical background. Background in algorithms at the level of CS 473 and mathematical maturity is expected. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
54730	Lecture-Discussion	CSS	3:30PM -4:45PM	TR	3025 Campus Instructional Facility	Chandrasekharan, E Saha, K	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Computational Social Science Section Info: In this course, we will explore how social behaviors are mediated by computational systems. Focusing on a combination of sociological foundations and recent advances in computational social science, natural language processing, and human-centered AI, we will learn to apply computational techniques to answer social science questions. Through this course, students will read and critique high-impact research papers, lead and engage in class discussions, work on implementing new methods during in-class lab sessions, and execute a group research project for their final paper. Prerequisites for the Course: The assignments and activities (i.e., research labs, reading reflections, research project) in this course are specifically aimed at students interested in performing computational social science research. As a result, we expect students enrolled in this course to have a good grasp of python, statistics, and basic data manipulation/analysis. Restrictions: Restricted to Grad students. Undergrads can apply for exceptions. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign.
57781	Lecture-Discussion	DHT	2:00PM -3:15PM	WF	1302 Siebel Center for Comp Sci	Hakkani Tur, D	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Conversational AI Section Info: The goal of this course is to cover advanced research topics about conversational AI systems and review founding papers as well as recent work in task-oriented dialog systems, open-domain and social conversational systems, and conversations with embodied systems. We will review previous work on component-wise approaches, as well as end-to-end systems based on large language models (such as ChatGPT), and discuss where they converge and diverge. The target audience is graduate students who plan to or are already working on these topics. As our time permits, I also plan to invite leading researchers in this field to present guest lectures. Students are expected to propose and work on a research project in one of these areas; we will discuss the proposals and progress throughout the course. In addition to preparing their final projects, students will present paper reviews, and will do a peer review of others' proposals and project reports. 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 MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
67234	Lecture-Discussion	EVS	2:00PM -3:15PM	TR	1214 Siebel Center for Comp Sci	Solomonik, E	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Tensor Computations Section Info: The course will cover theory, algorithms, and some applications of tensors and tensor decompositions. The course will first review and explore relevant topics in numerical linear algebra and nonlinear optimization, including iterative solvers and preconditioning, low rank approximation, as well as randomized techniques (sketching). Properties of decompositions and eigenvalues of tensors, as well as algorithms for computing these will be covered in detail. The course will also survey algorithms for inexact contraction of large tensor networks and methods for dynamical low-rank simulation with tensor decompositions. Prerequisites: familiarity with numerical linear algebra, and algorithms (e.g. CS 450, and CS 374 or CS 473) 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.
63912	Lecture-Discussion	FLA	9:30AM -10:45AM	MW	0216 Siebel Center for Comp Sci	Lai, F	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Systems for GenAI Section Info: This course will introduce the key concepts and the state-of-the-art in practical, scalable, and fault-tolerant software systems for emerging Generative AI (GenAI). It will encourage you to design system tools or apply existing ones in your own research. Topics cover basics of GenAI models from a systems perspective; systems for GenAI lifecycle (pre-training, training, fine-tuning/alignment, inference serving, and grounding); etc. The course will be a mix of lectures, student presentations, seminar-style discussions, and a semester-long project on GenAI topics. Prerequisites: This course is NOT focused on AI methods but on building software systems to support AI methods in practice. Students are expected to have good programming skills and must have taken at least one undergraduate-level systems-related course (from operating systems, databases, distributed systems, or networking). Having an undergraduate ML/AI course is helpful but not required. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
36011	Lecture-Discussion	JBR	11:00AM -12:15PM	TR	1310 Digital Computer Laboratory	Jabbarvand Behrouz, R	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: ML for Software Engineering Section Info: 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. 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.
60407	Discussion/ Recitation Online Lecture	JCR JCR	11:00AM -12:15PM 11:00AM -12:15PM	T R	ARR Illini Center n.a.	Jabbarvand Behrouz, R Jabbarvand Behrouz, R	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: ML for Software Engineering Section Info: This section is intended for Chicago MCS only. There may be online and in person components. You are responsible for completing homeworks, quizzes, and any in person activities that are required. Please speak with your professor regarding expectations. 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. Weekly in-person meeting in Classroom B at 200 S. Wacker Dr. Chicago. Restriction(s): Restricted to MCS: Computer Sci OFF - UIUC.
40105	Lecture-Discussion	JFG	11:00AM -12:15PM	TR	1043 Sidney Lu Mech Engr Bldg	Granha Jeronimo, F	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Expansion, Codes, and Quantum Section Info: This graduate topics course aims to take students from the rudiments to some parts of the research frontiers of expansion, coding theory, and optimization from a classical and quantum perspective. Main Topics Expansion: Expander graphs combine two opposing properties of being well-connected yet sparse. This powerful combination leads to various applications in CS (and mathematics), such as error correction, hardness of approximation, fast algorithms, sampling, etc. Recently, several notions of high-dimensional expansion appeared, leading to exciting discoveries. Coding theory: Codes are "robust" collections of strings that not only have implications for protection against errors in communication and storage but also have connections to diverse fields such as complexity theory, expansion, etc. Recently, many breakthrough constructions of codes were discovered, such as explicit binary codes close to the GV bound, good LTCs, and good qLDPC. Optimization: Convex programming, in particular, linear programming (LP) and semi-definite programming (SDP), are at the heart of many efficient algorithms. The use of powerful SDP hierarchies, such as the Sum-of-Squares hierarchy, has recently found many algorithmic applications. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
70878	Lecture-Discussion	JH	12:30PM -1:45PM	WF	1035 Campus Instructional Facility	Han, J	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Text Mining Large Lang. Models Section Info: This course provides an in-depth introduction and investigation on recent developments of text mining and natural language processing methods with large language models. We introduce the primitives of representation learning and large language models (LLMs) and focus on the research frontiers on representation learning and LLM for automated information extraction, knowledge graph construction, ontology enrichment, RAG (retrieval augmented generation), and theme-specific LLM construction and exploration. Restriction(s) Restricted to Graduate - Urbana-Champaign. Consent to instructor for CS-major senior/junior Undergraduate. Not intended for First Time Freshman students. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign.
36002	Lecture-Discussion	KCC	2:00PM -3:15PM	TR	0222 Siebel Center for Comp Sci	Chang, K	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Understanding LLMs AKA ChatGPT Section Info: Generative AI has transformed NLP and established a new paradigm upon large language models (LLMs) for building autonomous agents that can interact, acquire, and process knowledge for humans. While LLMs like ChatGPT have created real buzz, our understanding of these large and complex models are limited: How does it work? What can it do? Can we use LLMs to automate daily tasks of knowledge acquisition, such as reading technical papers, browsing websites, and checking emails? This class will take a laboratory and collaborative approach to learn LLMs by doing research: For "understanding"- We will characterize the behavior of LLMs. For "using": We will program LLMs as software agents that can execute these tasks on behalf of users. Objective: Publishable research papers. Format: hands-on search in small groups of 1-2 leads and 1-2 apprentices. Prerequisites: 1) Lead- Research experience in NLP/ML/DM (having published in ACL/EMNLP or similar venues) and 2) Collaborator/Apprentice: CS447 and strong CS background. Please apply at https://forms.gle/6u8YCu7xJmvqWZ4R7 for instructor consent. 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 MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
57715	Lecture-Discussion	KD	12:30PM -1:45PM	TR	2406 Siebel Center for Comp Sci	Kang, D	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: How to Do Research Section Info: In this course, you will learn fundamental skills on how to do research (limited to SysNet, DIAS, and AI). You will learn basic research skills including: reading a paper in-depth, doing a literature search, making presentations, writing papers, and running experiments. The majority of the course will be doing a hands-on research project. Prerequisites: an upper-level graduate course in SysNet, DIAS, or AI. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
72124	Lecture-Discussion	KKH	9:30AM -10:45AM	MW	2039 Campus Instructional Facility	Hauser, K	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: ADV Comp. Topics in Robotics Section Info: Advanced computational topics in robotics A graduate survey course on robotics, focusing on mathematic foundations, algorithms, machine learning, and integrating software and hardware systems. Lecture topics will include physics simulation, collision checking, motion planning, probabilistic filtering and tracking, 3D perception, and robot learning. Students will read current academic papers and carry out a semester-long, team-based project. Special restrictions: no limits on CS and non-CS enrollment. Prerequisite CS 225. https://cs598kkh2022.web.illinois.edu/ 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.
62086	Lecture-Discussion	KMC	11:00AM -12:15PM	TR	1302 Siebel Center for Comp Sci	Lewis, C	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Found. for Comp. Edu. Research Section Info: Introduction to computing education research, including: relevant cognitive, social, and cultural theories; assessment and evaluation of computing learning and attitudes; major research findings and pedagogical approaches; and current state of the field. Not for Online MCS students. Not intended fo0r CS PhD students. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
35989	Lecture-Discussion	KPH	11:00AM -12:15PM	TR	1302 Siebel Center for Comp Sci	Lewis, C	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Foundations for Comp. Ed Res. Section Info: Introduction to computing education research, including: relevant cognitive, social, and cultural theories; assessment and evaluation of computing learning and attitudes; major research findings and pedagogical approaches; and current state of the field. PhD students only Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to PHD:Computer Science -UIUC. Not intended for First Time Freshman students.
67238	Lecture-Discussion	MRT	11:00AM -12:15PM	MW	1214 Siebel Center for Comp Sci	Tahmasbi, M	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Quantum Information in TCS Section Info: This course will explore the fundamental concepts in the theory of quantum information and their applications in fields like complexity theory, statistics, and cryptography. We shall study how information processing tasks become fundamentally different in a quantum world. The course will cover the following broad topics: - Entropic quantities and their operational meaning: We will delve into the applications of information measures in quantum statistics and quantum cryptographic protocols, such as quantum key distribution. - Quantum error correction: We will discuss strategies for safeguarding quantum information against interference and noise. - Symmetry in quantum information processing: We will investigate mathematical objects such as geometric designs that respect certain symmetries in a quantum system, and study their applications in complexity theory and cryptography. - Quantum nonlocality: We will explore mathematical frameworks for analyzing unique correlations that arise from quantum mechanics. This course is aimed at master’s and graduate students who have a research interest in quantum information science or theoretical computer science. Prior knowledge of linear algebra concepts is necessary. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
46983	Lecture-Discussion	PEN	2:00PM -3:15PM	MW	2101 Everitt Laboratory	Peng, H	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: LLM Post-pretraining Section Info: Recent progress in open-source pretrained large language models (LLMs) have opened up new exciting opportunities for researchers to explore creative ideas, even when they may lack extensive resources for pretraining. This course delves into them through lectures and student-led discussions. We will cover continual pretraining, instruction fine-tuning, preference learning, alignment, efficiency optimization, evaluation, and so on. Though this course is primarily designed for graduate students, motivated undergraduates with suitable backgrounds are also welcome. Prior research experience in related fields (such as natural language processing, machine learning, vision, etc.), strong skills for paper reading and presentation, proficiency in Python and modern deep learning frameworks are assumed. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70683	Online	PSO	ARRANGED	n.a.	n.a.	Liang, F	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Practical Statistical Learning Section Info: This section is only for students that are in the Computer Science Online MCS/MCS-DS Program offered on the Coursera platform. Additional ProctorU fees may apply. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC.
40106	Lecture-Discussion	SFS	3:30PM -4:45PM	TR	2406 Siebel Center for Comp Sci	Sultana, S	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Computing & Global Developmnt Section Info: Computing and Global Development is a course that examines the intersection of computing technologies and international development. It explores how computing can be used to address global challenges, such as poverty, inequality, and climate change. The course draws on a variety of academic disciplines, including Information and Communication Technology and Development (ICTD), Human-Computer Interaction (HCI), Development Sociology, Science and Technology Studies (STS), and political economy. The course also teaches students how to design and evaluate ICT-based interventions for development. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
57783	Lecture-Discussion	SHW	2:00PM -3:15PM	TR	0216 Siebel Center for Comp Sci	Wang, S	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: 3-D Vision Section Info: For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
73072	Lecture-Discussion	SMC	12:30PM -1:45PM	TR	1043 Sidney Lu Mech Engr Bldg	Cobb, C Sterman, S	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Methodological Pluralism Section Info: This graduate-level seminar covers the concept of methodological pluralism in human-computer interaction research. We will examine research methods, philosophies of research, and diverse ways of knowing to build foundational concepts and analytical skills for engaging in and understanding interdisciplinary research in computer science. Students will read research papers and methodological theory and engage in critical writing, group discussion, and oral presentations. This is a course designed primarily for PhD students in the Interactive Computing area. It may additionally be appropriate for PhD students in other areas, or advanced master’s students seeking to enter graduate research. This course does not assume prior research experience or experience in HCI. Section SMC is for CS on-campus MCS students only. Section SPH is for CS and I-school PhD students. There will be no undergrad overrides for either section. 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.
49828	Lecture-Discussion	SPH	12:30PM -1:45PM	TR	1043 Sidney Lu Mech Engr Bldg	Cobb, C Sterman, S	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Methodological Pluralism Section Info: This graduate-level seminar covers the concept of methodological pluralism in human-computer interaction research. We will examine research methods, philosophies of research, and diverse ways of knowing to build foundational concepts and analytical skills for engaging in and understanding interdisciplinary research in computer science. Students will read research papers and methodological theory and engage in critical writing, group discussion, and oral presentations. This is a course designed primarily for PhD students in the Interactive Computing area. It may additionally be appropriate for PhD students in other areas, or advanced master’s students seeking to enter graduate research. This course does not assume prior research experience or experience in HCI. Section SMC is for CS on-campus MCS students only. Section SPH is for CS and I-school PhD students. There will be no undergrad overrides for either section. 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.
36022	Lecture-Discussion	TAL	9:30AM -10:45AM	TR	1214 Siebel Center for Comp Sci	August, T	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Lang., Interfaces, and Comm. Section Info: In this course, we will explore recent advances in computing for augmenting natural language. We will cover foundational theories of communication and investigate new advances in interactive computing and artificial intelligence that aim to improve how we communicate with one another. We will learn how to build and evaluate human-centered language technologies for improving human communication (e.g., intelligent reading and writing tools). We will focus class on in-person discussions around research papers and scientific advances. Students will be expected to read papers, post reading reflections, share and comment on papers and ideas, and lead at least one class discussion. The class will culminate in a research project focused on building or evaluating interactive systems and current language technologies. This course is specifically aimed at graduate students interested in conducting research in interactive computing and natural language processing. As such, students should have a good grasp of python, statistics, and data analysis. Classes will not be recorded. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
57782	Lecture-Discussion	TZ	11:00AM -12:15PM	TR	2406 Siebel Center for Comp Sci	Zhang, T	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: ML Algorithms for LLMs Section Info: This course is an in-depth study of advanced machine learning algorithms used in the current development of large language models (LLMs). The course covers a wide range of topics, starting with mathematical models for sequence generation, and important neural network architectures with a focus on transformers. We will examine issues such as context length, explainability, optimization strategies, and variants of transformer models for image generation and understanding. We will then investigate variants of transformer based language models, along with algorithms for prompt engineering and improving reasoning capability. Other topics include ML techniques used in studying LLM safety, hallucination, fine-tuning of LLMs, alignment (reinforcement learning from human feedback), multimodal LLMs, and common methods for accelerating training and inference. Prerequisites: This course focuses on the understanding of machine learning algorithms in the development of LLMs, rather than applications of LLMs. Therefore students are expected to have a solid foundation in machine learning (especially deep learning), and programming in python. Prior project experience with machine learning, natural language processing, and PyTorch is also needed. The students should also have solid background in mathematics, and are expected to be able to understand abstract notations in advanced probability and linear algebra without any difficulty. For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
42393	Lecture-Discussion	WSI	12:30PM -1:45PM	WF	1214 Siebel Center for Comp Sci	Vasisht, D	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: ADV Wireless Networks and IOT Section Info: This graduate-level seminar will cover the latest research in the domain of wireless networks and the Internet of Things. We will discuss basic principles in wireless networks and how wireless networks must evolve for the new context of the Internet of Things. 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. In this class, we will also have guest lectures from experts in academia and/or industry on a subset of the themes described below to get deeper into how the research problems that we are discussing impact the world out there. Preferred pre-reqs: One of CS 438, CS 439, CS 437, CS 435 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.
36009	Lecture-Discussion	YOU	12:30PM -1:45PM	WF	1304 Siebel Center for Comp Sci	You, J	Part of Term: 1 Date Range: 08/26/24-12/11/24 Credit: 4 hours Section Title: Deep Learning with Graphs Section Info: This course delves into the exciting field of deep learning for graph-structured data. This course is designed to equip students with an understanding of fundamental principles, classic models, and cutting-edge algorithms, along with practical applications. The curriculum will commence with an exploration of the foundationals, encompassing graph concepts, deep learning fundamentals, node embeddings, and graph neural networks. Building upon this groundwork, the course will progress to advanced topics, including the practical guide for GNN implementation, theories of graph neural networks, and specialized areas such as heterogeneous graphs, knowledge graphs, reasoning mechanisms, and subgraph mining. Moreover, the course will delve into contemporary applications, including graph-based recommender systems, generative models for graphs, graph transformers, and building scalable graph learning systems. The instructional approach will blend traditional lectures, student-led presentations, interactive seminar-style discussions, and collaborative semester-long projects. Through this multifaceted approach, students will not only grasp the knowledge but also develop analytical skills by reading and critiquing research papers, discussing and presenting ideas, and contributing to collaborative projects.For up-to-date information about CS course restrictions, please view the following link for restrictions and release dates: http://go.cs.illinois.edu/csregister. 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.