IE 498

Spring 2020 All Classes

All Classes

Special Topics

SHOW FINAL EXAM SCHEDULE

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.

My Favorites

My Schedule

Section Status updates every 10 minutes.

IE 498 class schedule data for spring 2020
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
70964	Lecture	ET	6:00PM -9:00PM	R	ARR Illini Center	Lariviere, D	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 3 hours Section Title: Electronic Trading Section Info: The purpose of this course is to investigate the exact nature of order matching and routing at the compute-packet level in most exchanges. Not knowing the nature the interfaces has led to many "good fit" predictive models. However, they are often, predicting the past! Analyses need to adjust for speed and time-stamps. The course will address these issues. However, it should be stressed that the course does not purport nor intend to examine nor propose "trading strategies." This course is only intended for students enrolled in the City Scholars program. Restriction(s): Restricted to O/C Engineering City Scholars students.
70712	Lecture	JS	9:30AM -10:50AM	TR	119 Materials Science & Eng Bld	Sirignano, J	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 3 hours Section Title: Deep Learning Section Info: Prerequisites: IE 300 or equivalent course This course provides an introduction to neural networks and recent advances in deep learning. Topics include training and implementation of neural networks, convolution neural networks, recurrent neural networks (LSTM and gated recurrent), residual networks, reinforcement learning, and Q-learning with neural networks. A part of the course will especially focus on recent work in deep reinforcemcent learning. The course will also cover the deep learning library PyTorch and how to train neural networks using GPUs and GPU clusters. Restriction(s): Not intended for students with Freshman, Sophomore, or Junior class standing.
48736	Lecture	YZ	2:00PM -3:20PM	TR	1304 Siebel Center for Comp Sci	Zhou, Y	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Machine Learning for Oprn Rsch Section Info: Prerequisites: CS 101 or equivalent, IE 410 or equivalent, and MATH 415 or equivalent Description: 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): Not intended for students with Freshman, Sophomore, or Junior class standing.