IE 398

Spring 2021 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.

May be repeated in the same or separate terms if topics vary.

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IE 398 class schedule data for spring 2021
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	70559	Online	CV	2:00PM -3:20PM	TR	n.a.	Vogiatzis, C	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Simulation Section Info: Course prerequisites: CS 101 and IE 300. Course description: Use of discrete-event simulation in the modeling and analysis of complex systems subject to uncertainty. At the end of the course, the students should be able to develop simulation models of complex, real-life systems; design simulation experiments; analyze and interpret the results of the simulation; and effectively organize and present simulation-based projects. The topics of the course include input modeling, selecting probability distributions, and generating random variables, sensitivity analysis, simulation optimization, and reporting and analyzing simulation outputs. Restriction(s): Restricted to students with Junior or Senior class standing. Restricted to Industrial Engineering major(s).
	64090	Online	ML	7:00PM -8:50PM	TR	n.a.	Zhou, Y	Part of Term: 1 Date Range: 01/25/21-05/05/21 Credit: 3 hours Section Title: Machine Learning for Oprn Rsch Section Info: Prerequisites: CS 101, IE, 300, MATH 241, and MATH 415 (or the equivalents). Course Description: This course is an introductory/intermediate level machine learning course for senior undergraduate and junior graduate students. This lecture course will mainly focus on machine learning algorithmic development (and some theoretical analysis for the second part of the course). The students should have programming skills to implement the algorithms. This course contains two parts: (1) In the first part, we will discuss the basics of supervised (regression and classification) and unsupervised learning (clustering and dimension reduction). Then, we will learn modern topics such as graphical models, EM algorithm, neural networks, semi- supervised learning, and stochastic optimization for training web-scale data. We will unveil the blackbox for each machine learning algorithm and provide the details on how the algorithm was developed. (2) In the second part, we will move from machine learning to sequential learning & decision-making, covering hot topics such as multi-armed bandit and reinforcement learning. For this part, we will provide more theoretical analysis of the algorithms. Restriction(s): Restricted to students with Junior or Senior class standing.