IE 598

Fall 2026 Part of Term 1

Part of Term 1
Aug 24-Dec 9

Special Topics

Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.

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IE 598 class schedule data for fall 2026
	Status		CRN	Type	Section	Time	Day	Location	Instructor	Section Details
			50019	Lecture	RS	3:30PM -4:50PM	TR	Location Pending	Sreenivas, R	Availability: Open (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Quantum Computing for ISE Section Info: Course prerequisites: IE 410 or equivalent course on Stochastic Processes, IE 411 or equivalent course on Math Programming. Course description: This course introduces the mathematical foundations and practical applications of quantum computing. Students will develop a rigorous understanding of quantum mechanics as it applies to computation, and will explore both current quantum hardware and leading quantum algorithms relevant to optimization and machine learning— two domains of central importance in ISE. No prior background in quantum mechanics is assumed. The course is self-contained and designed to equip ISE students with the conceptual and computational tools needed to engage with. quantum approaches to complex engineering problems. Restriction(s): Restricted to Graduate - Urbana-Champaign.
			60476	Lecture-Discussion	YYL	2:00PM -3:20PM	TR	Lincoln Hall	Li, Y	Availability: Open (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: RL & Learning-based Control Section Info: Course description: This course introduces algorithms for reinforcement learning (RL) and learning-based control. The first part of the course (approximately one third of the term) covers foundational topics, including Markov decision processes and their connections to stochastic control, dynamic programming, rollout algorithms and model predictive control, partially observable MDPs and output-feedback control, multi-armed bandits, policy evaluation and policy gradient methods in RL and control. The remainder of the course addresses advanced topics such as function approximations and deep RL, model-based RL and statistical system identification, contextual bandits and contextual RL, online nonstationary RL and control, Monte Carlo Tree Search, imitation learning, reinforcement learning with human feedback (RLHF), safe RL and constrained learning-based control, meta-RL and switching control, and connections to large language models. Coverage of the advanced topics may vary depending on time availability. The course emphasizes algorithm design, basic theoretical guarantees and key proof ideas, with concepts illustrated through small- to mid-scale simulations. Prerequisite: MATH 257, MATH 415, or equivalent course on linear algebra and MATH 362, MATH 461, or equivalent course on probability. Previous knowledge of random processes, machine learning, and/or control systems is strongly recommended. Restriction(s): Restricted to Graduate - Urbana-Champaign.