IE 598

Fall 2016 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.

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 fall 2016
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
66928	Lecture	AC	9:00AM -10:20AM	TR	136 Loomis Laboratory	Chronopoulou, A	Part of Term: 1 Date Range: 08/22/16-12/07/16 Credit: 4 hours Section Title: Stat Infer for Stoch Sys Section Info: Statistical Inference for Stochastic Systems with Long Memory. Prerequisites: Graduate-level courses on Stochastic Porcesses and on Probability Theory. The goal of this course is to study stochastic systems with long-range dependence. Specifically, the focus will be on parameter estimation, filtering, and simulation techniques for fully and partially observed non Markovian systems in discrete and continuous time. Restriction(s): Restricted to Graduate - Urbana-Champaign.
41718	Lecture-Discussion	JG	9:30AM -10:50AM	TR	1103 Siebel Center for Comp Sci	Garg, J	Part of Term: 1 Date Range: 08/22/16-12/07/16 Credit: 4 hours Section Title: Games, Mkts, & Mathmtcl Prog Section Info: Course Prerequisites: IE 310 or equivalent; basic knowledge of optimization, probability, and linear algebra; mathematical maturity.This course will introduce students to the theory of games and markets and their strong connections to mathematical programming techniques. It will include solution concepts in game theory such as Nash equilibrium and correlated equilibrium, their computation; zero-sum games and minimax theorem; extensive form games; repeated games; competitive equilibrium in markets; utility maximization; strategic analysis; among others. It will be shown that many problems in these areas can be formulated as network flow, linear programming (LP), convex programming (CP), and complementarity (LCP, NCP) problems. The course will also touch upon the topics of fair division, resource allocation, bargaining and mechanism design without money. Restriction(s): Restricted to Graduate - Urbana-Champaign.
66415	Lecture	JS	4:00PM -5:20PM	MW	153 Mechanical Engineering Bldg	Sirignano, J	Part of Term: 1 Date Range: 08/22/16-12/07/16 Credit: 4 hours Section Title: Neural Ntwks and Deep Learning Section Info: Prerequisites: IE 510, CS 446 or equivalents. 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, reinforcement learning, and Q-learning with neural networks. Applications are primarily drawn from image classification. Restriction(s): Restricted to Graduate - Urbana-Champaign.
50019	Lecture-Discussion	NH	11:00AM -12:20PM	TR	206 Transportation Building	He, N	Part of Term: 1 Date Range: 08/22/16-12/07/16 Credit: 3 hours Section Title: Big Data Optimization Section Info: Course prerequisites: Students are expected to have strong knowledge of linear algebra, realanalysis and probability theory. Some prior exposure to optimization 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 aim 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 resarch in large-scale optimization and machine learning. Restriction(s): Restricted to Graduate - Urbana-Champaign.
60476	Lecture-Discussion	SO	2:00PM -3:20PM	TR	206 Transportation Building	Oh, S	Part of Term: 1 Date Range: 08/22/16-12/07/16 Credit: 4 hours Section Title: Inference in Graphical Models Section Info: Introduction to statistical inference with probabilistic graphical models and low-complexity inference algorithms. In particular, we will treat the following methods: message-passing algorithms, belief propagation, loopy-belief propagation, variational methods, Markov chain Monte Carlo methods, learning structure. Applications and examples will include: Gaussian models, linear dynamical systems and hidden Markov models (forward-backward algorithm, Kalman filtering, Viterbi algorithm), computer vision, and machine learning (clustering, classification). Restriction(s): Restricted to Graduate - Urbana-Champaign.