IE 598

Fall 2018 All Classes

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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.

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IE 598 class schedule data for fall 2018
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
70577	Lecture	MLF	8:00AM -9:20AM	TR	112 Transportation Building	Lane, M	Part of Term: A Date Range: 08/27/18-10/19/18 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.
70387	Discussion/ Recitation Discussion/ Recitation Lecture	PD PD PD	10:00AM -10:50AM 11:00AM -11:50AM 5:00PM -5:50PM	F F W	Location Pending Location Pending Location Pending	Noonan, J Noonan, J Noonan, J	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 2 hours Section Title: MSFE Professional Development Section Info: This course will provide MSFE students with content, instruction, and experience that will increase their likelihood of success as (1) student (2) job seeker and applicant and (3) professional - particularly in th context of business. Restriction(s): Restricted to MS: Financial Engineering.
70605	Lecture	QS	3:30PM -4:50PM	TR	260 Mechanical Engineering Bldg	Stolyar, A	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Queueing Systems Section Info: Course Prerequisite: IE 410 or equivalent graduate stocahstic processes course. Over the last 100 years queueing theory has become a powerful tool for analysis and design of a wide range of engineering systems. Modern applications include: telecommunication, informaton and computing systems; network clouds; inventory control; service systems; and others. This course is an introduction to queueing systems and their applications in engineering. Topics include both classical single-stage models and queueing networks. Students will learn how to apply key ideas and methods of queueing theory, such as: Markov processes, embedded Markov chains, PASTA property, reversibility, product-form stationary distributions, stochasic stability, asymptotic analysis. Restriction(s): Restricted to Graduate - Urbana-Champaign.