IE 598

Fall 2023 All Classes

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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.

1 to 4 graduate hours. No professional credit. 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 2023
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
65360	Lecture-Discussion	CDM	2:00PM -3:20PM	TR	1018 Literatures, Cultures, & Ling	Ray Chaudhury, B	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Collective Decision Making Section Info: Prerequisites: CS 101, IE 300, and IE 411 or equivalent courses. This course focuses on decision-making in the presence of multiple agents with preferences. This would cover topics on optimized democracy (voting rules, axioms, liquid democracy), embedded ethics (biases in algorithms, societal effects of algorithms, algorithmic fairness), similar paradigms in ML (federated learning), and related topics from economics (fair division, general equilibrium theory, and stable matching).
52048	Online Lecture	CSQ	5:30PM -8:30PM	W	n.a.	Lariviere, D	Part of Term: 1 Date Range: 09/19/23-11/13/23 Credit: 1 hours Section Title: Computer Science for Quants Section Info: Prerequisite: some prior programming experience. Virtually all aspects of modern science and engineering are now heavily reliant on both computer science and especially software programming. Software engineering has become an increasingly useful and often even essential skill and discipline for those intending to work in virtually all STEM fields, regardless of major. Students from a variety of majors learn how to write computer programs, but often lack a fundamental understanding of exactly how the programming languages, computer software, or hardware actually function. This course aims to fix that.
61631	Lecture	DPR	2:00PM -3:20PM	TR	1304 Siebel Center for Comp Sci	Li, Y	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Dynamic Prog & Reinforce Learn Section Info: Prerequisite: MATH 257, MATH 415, or equivalent course on linear algebra and MATH 362, MATH 461, or equivalent course on probability. Course Description: This course will discuss the algorithm design, theoretical analysis, and simulations of dynamic programming (DP) and reinforcement learning (RL) in either finite horizon or infinite horizon, with either full observations or partial observations. Most discussions will focus on the tabular case. DP and RL with function approximations will also be introduced if time permits. Restriction(s): Restricted to Graduate - Urbana-Champaign.