IE 598

Fall 2021 All Classes

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Credit: 0 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 2021
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
72015	Lecture-Discussion	AW	9:00AM -10:20AM	MW	1302 Everitt Laboratory	Wooldridge, A	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: Job and Organization Design Section Info: Please Note Due to the nature of this class, if there is any student that cannot be in person, the class will be completely online. Prerequisites: IE 340 credit is recommended. The purpose of this course is to understand models and theories of job and organization job, to be able to answer the questions “What makes for a good job?” and “What makes for a bad job?” Students will be able to apply models and theories of job and organization design to the analysis and redesign of jobs – to figure out how to improve a “bad” job, and ideally make it a good one. Finally, we will talk about processes to use to implement job redesigns. The IE 598 offering will include all of the material and assignments as IE 498, in addition to each student developing their own research proposal based on course material. Restriction(s): Restricted to Graduate - Urbana-Champaign.
76225	Online	CSQ	6:30PM -9:30PM	W	n.a.	Lariviere, D	Date Range: 10/27/21-11/23/21 Credit: 1 hours Section Title: Computer Science for Quants Section Info: Prerequisite: 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.
76214	Online Lecture	HFT	6:30PM -9:30PM	R	n.a.	Lariviere, D	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: High Frequency Trading Section Info: Electronic Trading, informally known as “High Frequency Trading”, will teach students both the core concepts and underlying mechanics of, step by step, message by message, bit for bit, exactly how trillions of dollars in notional value are automatically traded daily around the globe. Electronic Trading will provide students with an exciting introduction both to the modern world of automated finance and to many exciting technologies that power it. Where does the “actual” real-time price of a particular asset come from at any point in time? How exactly is it being calculated and by who or what? Is there even a single price or are there multiple, and are any of those prices actually correct? How quickly can the price change or suddenly stop changing after plummeting? How do markets break or pricing models fail? Restriction(s): Restricted to Graduate - Urbana-Champaign.
65637	Lecture-Discussion	YZ	3:30PM -4:50PM	TR	112 Transportation Building	Zhou, Y	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: Online Lrning & Decisn Making Section Info: Prerequisites: background in basic probability theory. linear algebra, and algorithm design and analysis. This course covers several foundational topics in online learning and sequential decision making under uncertainty, a subject on the intersection of algorithms, machine learning, and operations research. Such problems have wide applications in online advertising, recommendation systems, crowdsourcing, revenue management, etc. In this course, we will study the problems that usually feature the tension between how to collect data and utilize the data to make optimal sequential decisions (a.k.a. the exploration and exploitation dilemma). We cover both fundamental results and research frontiers. We focus on algorithmic results, and introduce lower bounds as well. Restriction(s): Restricted to Graduate - Urbana-Champaign.
76023	Online Lecture	YZO	ARRANGED	n.a.	n.a.	Zhou, Y	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: Online Lrning & Decisn Making Section Info: Prerequisites: background in basic probability theory. linear algebra, and algorithm design and analysis. This course covers several foundational topics in online learning and sequential decision making under uncertainty, a subject on the intersection of algorithms, machine learning, and operations research. Such problems have wide applications in online advertising, recommendation systems, crowdsourcing, revenue management, etc. In this course, we will study the problems that usually feature the tension between how to collect data and utilize the data to make optimal sequential decisions (a.k.a. the exploration and exploitation dilemma). We cover both fundamental results and research frontiers. We focus on algorithmic results, and introduce lower bounds as well. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for NDEG:Grad Nondegree-CE-UIUC.