IE 598

Spring 2018 All Classes

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.

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 spring 2018
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: 01/16/18-05/02/18 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.
67121	Lecture	JS2	9:30AM -10:50AM	TR	106B3 Engineering Hall	Sirignano, J	Part of Term: 1 Date Range: 01/16/18-05/02/18 Special Approval: Instructor Approval Required Credit: 4 hours Section Title: Deep Learning II Section Info: Prerequisites: IE 598 Deep Learning or equivalent. Students should contact the instructor, Justin Sirignano (jasirign@illinois.edu), if interested in enrolling in this course. This is a project course. Students will work in small teams on deep learning applications in (1) reinforcement learning, (2) image recognition, or (3) high-frequency models of financial markets. The course will provide an introduction to distributed training of neural networks and Distributed TensorFlow. GPU hours will be provided to the class. Restriction(s): Restricted to Graduate - Urbana-Champaign.
67120	Lecture	KC	11:00AM -12:20PM	TR	106B3 Engineering Hall	Chandrasekaran, K	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Combinatorial Optimization Section Info: Prerequisites: Working knowledge in Linear Programming, Graph Theory, Linear Algebra. This course will cover a series of topics in combinatorial optimization. The emphasis will be on polyhedral theory and structural results. Specific topics to be covered include: Matchings, b-matchings, T-joins, Branchings, Matroids, Matroid Intersections, Polymatroids, Submodular Functions, Directed Cuts, Multi-flows. Restriction(s): Restricted to Graduate - Urbana-Champaign.
60582	Lecture	LM	8:00AM -9:20AM	TR	1103 Siebel Center for Comp Sci	Marla, L	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Large-scale Ntwrk Optimization Section Info: Prerequisites: Knowledge in linear programming duality is required. Description: Shortest paths on acyclic networks, labeling algorithms; Generalized shortest paths and labeling algorithms; Multi-commodity flows (and differences from the minimum-cost network flow problem), branch-and-price and cut; Lagrangean relaxation, formulations and solution techniques; Airline Schedule Planning models – Set-covering and set-partitioning problem formulations; composite variable modeling (Case studies on Crew Scheduling and Airline Fleet Assignment); Combining multiple multi-commodity flow formulations; Data-driven Modeling, Simulation-optimization frameworks (Case study: Ambulance Allocation); Large-Scale neighborhood search (Case study: Vehicle routing and metaheuristics); Stochastic modeling in large-scale integer programs (Case study: Stochastic Crew Scheduling); Robust Optimization for integer programs; Operational Learning (Case study: Big Data Newsvendor problem). The course will present the concepts through real-world case-studies drawn from airline schedule planning, freight and last-mile logistics, emergency systems, vehicle routing and newsvendor problems. Restriction(s): Restricted to Graduate - Urbana-Champaign.
67114	Lecture	NH	4:00PM -5:20PM	MW	203 Transportation Building	He, N	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Big Data Optimization Section Info: Students are expected to have strong working knowledge iof linear algebra, real analysis, and probability theory. Some prior exposure to optimization and algorithms at a graduate level is preferred. The course will cover a variety of advanced topics in optimization theory, algorithms and applications in machine learning. The key aim of this course is to expose students to modern algorithmic developments in convex optimization (smooth, non-smooth, deterministic, stochastic, and online) and bring them near the frontier of current research in large-scale optimization and machine learning. Restriction(s): Restricted to Graduate - Urbana-Champaign.
64238	Lecture-Discussion	XCD	5:00PM -6:20PM	TR	204 Transportation Building	Chen, X	Part of Term: 1 Date Range: 01/16/18-05/02/18 Credit: 4 hours Section Title: Dynamic Optimization Section Info: Prerequisites: IE 411, IE 410 or equivalent courses on stochastic processes and deterministic optimization. The course covers the bsic modeling and solution techniques for sequential decision making problems under uncertainty including dynamic programming and stochastic programming modeling, theory, algorithms and approximations. Applications are drawn from economics, finance, operations management and engineering. Restriction(s): Restricted to Graduate - Urbana-Champaign.