IE 498

Fall 2019 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 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary to a maximum of 9 hours.

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IE 498 class schedule data for fall 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
72016	Lecture-Discussion	AW	9:00AM -10:20AM	MW	Loomis Laboratory	Wooldridge, A	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 3 hours Section Title: Job and Organization Design Section Info: 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. Restriction(s): Restricted to students with Senior class standing. Restricted to Graduate - Urbana-Champaign.
72634	Lecture-Discussion	YZ1	3:30PM -4:50PM	TR	Transportation Building	Zhou, Y	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Online Learning Section Info: Prerequisite: Basic probability theory, linear algebra, and algorithm design. Course Descriptions: Probability tools such as central limiting theorem and concentration inequalities, multi-armed bandits and the UCB algorithms, applications to assortment optimization (multinomial logit bandits), contexual bandits, linear contexual bandits, adversarial bandits and the multiplicative weights method, Markov decision processes and reinforcement learning (RL), model-based and model-free RL algorithms, reinforcement learning for large state spaces. Restriction(s): Restricted to Graduate - Urbana-Champaign.
72635	Lecture-Discussion	YZ2	3:30PM -4:50PM	TR	Transportation Building	Zhou, Y	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 3 hours Section Title: Online Learning Section Info: Prerequisite: Basic probability theory, linear algebra, and algorithm design. Course Descriptions: Probability tools such as central limiting theorem and concentration inequalities, multi-armed bandits and the UCB algorithms, applications to assortment optimization (multinomial logit bandits), contexual bandits, linear contexual bandits, adversarial bandits and the multiplicative weights method, Markov decision processes and reinforcement learning (RL), model-based and model-free RL algorithms, reinforcement learning for large state spaces. Restriction(s): Restricted to students with Senior class standing.