IE 598

Fall 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 fall 2019
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
72015	Lecture-Discussion	AW	9:00AM -10:20AM	MW	158 Loomis Laboratory	Wooldridge, A	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Job and Organization Design Section Info: Prerequisites: IE 340 credit is recommended. The purpose of this course is to understand models and theories of job and organization job, to be able to answer the questions “What makes for a good job?” and “What makes for a bad job?” Students will be able to apply models and theories of job and organization design to the analysis and redesign of jobs – to figure out how to improve a “bad” job, and ideally make it a good one. Finally, we will talk about processes to use to implement job redesigns. The IE 598 offering will include all of the material and assignments as IE 498, in addition to each student developing their own research proposal based on course material. Restriction(s): Restricted to Graduate - Urbana-Champaign.
71989	Lecture	EFF	2:00PM -3:20PM	TR	1302 Siebel Center for Comp Sci	Chronopoulou, A	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Estimation & Filtering in Fin Section Info: Prerequisites: IE 410 or equivalent course on stochastic processes. The focus of this course is on optimal online estimation and filtering methods for partially observed dynamical systems, such as Sequential Monte Carlo, MCMC, SMC2, and Particle MCMC. We will apply these techniques to the study of the volatility of a stock or an index. More specifically, we will introduce discrete and continuous time stochastic volatility models and study the dynamics of the volatility surface. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70577	Lecture	MLF	8:00AM -9:20AM	TR	112 Transportation Building	Lane, M	Part of Term: A Date Range: 08/26/19-10/18/19 Credit: 2 hours Section Title: Machine Learning in Fin Lab Section Info: Machine learning is an increasingly important tool in every financial engineer’s toolbox. Machine Learning includes the design and the study of algorithms that can learn from experience, improve their performance and make predictions. In this introductory course students will explore the main concepts behind several different machine learning algorithms and gain practical experience implementing them using Python and several of the most frequently used packages; pandas, NumPy, scikit-learn, etc.. Students will also learn how to construct and interpret their own machine learning models in Python. Restriction(s): Restricted to MS: Financial Engineering.
72075	Lecture	SE	5:00PM -6:20PM	TR	206 Transportation Building	Stolyar, A	Part of Term: 1 Date Range: 08/26/19-12/11/19 Credit: 4 hours Section Title: Service Engineering Section Info: Prerequisites: IE 410 or an equivalent course on stochastic processes. Restricted to Graduate students only. Description: Many systems, such as cloud data centers, communication networks, customer call/contact centers, healthcare services, can be modeled as service systems. Engineering an efficient service system involves many tasks, including system design, capacity planning and real-time control. In this course students will learn how to use stochastic models and methods to engineer and analyze service systems. A special emphasis is placed on large-scale systems and their asymptotic approximations (fluid, diffusion, mean-field), as well as on real-time resource allocation algorithms (routing and scheduling). Restriction(s): Restricted to Graduate - Urbana-Champaign.