IE 598

Spring 2019 All Classes

All Classes

Special Topics

SHOW FINAL EXAM SCHEDULE

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.

My Favorites

My Schedule

Section Status updates every 10 minutes.

IE 598 class schedule data for spring 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
66173	Lecture	ET	6:00PM -9:00PM	R	A Illini Center	Lariviere, D	Part of Term: 1 Date Range: 01/14/19-05/01/19 Credit: 4 hours Section Title: Electronic Trading Section Info: Prerequisites: IE 522 and IE 523. 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." Restriction(s): Restricted to MS: Financial Engineering.
69688	Lecture	ODL	3:30PM -4:50PM	TR	204 Transportation Building	Sun, R	Part of Term: B Date Range: 03/11/19-05/01/19 Credit: 2 hours Section Title: Optim Theory for Deep Learning Section Info: Prerequisites: IE 510, ECE 490, or equivalent courses on introductory optimization. Mathematical optimization is a crucial component of modern machine learning. This course gives an overview of optimization theory and methods in deep learning, including algorithms, modeling and geometry. The goal is to demystify common optimization methods/tricks in deep learning. For algorithms, we discuss various popular methods such as initialization, BatchNorm, Adam, etc. For geometry, we discuss the optimization landscape of neural-network problems, which is closely related to the quality of the local minima. We also discuss how to formulate GANs, adversarial attacks, etc. from an optimization perspective. Restriction(s): Restricted to Graduate - Urbana-Champaign.
69147	Lecture-Discussion	XCL	5:00PM -6:20PM	TR	203 Transportation Building	Chen, X	Part of Term: 1 Date Range: 01/14/19-05/01/19 Credit: 4 hours Section Title: Logistics & Supply Chain Mgmt Section Info: Prerequisites: IE 410 and IE 411. In this course, we will explore the state-of-the-art logistics strategies, models, algorithms and tools for integrating the supply chain in ways that reduce system-wide costs, and improve system-wide service. Our main focus is on building mathematical models, developing efficient algorithms and performing rigorous analysis. For this purpose, we will cover theoretical foundations such as dynamic programming, convex analysis and lattice programming essential to analyze these models. Some recent related research will also be covered. Restriction(s): Restricted to Graduate - Urbana-Champaign.