IE 498

Fall 2021 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.

1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary to a maximum of 9 hours.

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IE 498 class schedule data for fall 2021
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
72016	Lecture-Discussion	AW	9:00AM -10:20AM	MW	1302 Everitt Laboratory	Wooldridge, A	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 3 hours Section Title: Job and Organization Design Section Info: Please Note Due to the nature of this class, if there is any student that cannot be in person, the class will be completely online. 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. Restriction(s): Not intended for students with Freshman, Sophomore, or Junior class standing.
70464	Online Lecture	DL1	8:00AM -9:20AM	TR	n.a.	Sowers, R	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 3 hours Section Title: Deep Learning Math & Appl Section Info: Prerequisite: CS 101/Python, IE 300 MATH 231, MATH 415, and MATH 285 (or equivalent courses). Description: We will understand the groundwork for Deep Learning networks. We will understand the structure of Deep Neural Networks and how to train them. We will cover the basics of feedforward, convolutional, and recurrent neural networks. We will implement codes for deep networks in PyTorch. Restriction(s): Restricted to Civil Engineering or Computer Engineering or Computer Science or Electrical Engineering or Engineering Mechanics or Engineering Physics or Industrial Engineering or Materials Science & Engr or Mechanical Engineering or Chemical Engineering or Applied Mathematics or Statistics or Bioengineering or Mathematics or Actuarial Mathematics or Aerospace Engineering or Agricultural & Biological Engr or Nuclear, Plasma, Radiolgc Engr or Systems Engineering and Design major(s). Restricted to students with Junior or Senior class standing.
70465	Online Lecture	DL2	8:00AM -9:20AM	TR	n.a.	Sowers, R	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: Deep Learning Math & Appl Section Info: Prerequisite: CS 101/Python, IE 300 MATH 231, MATH 415, and MATH 285 (or equivalent courses). Description: We will understand the groundwork for Deep Learning networks. We will understand the structure of Deep Neural Networks and how to train them. We will cover the basics of feedforward, convolutional, and recurrent neural networks. We will implement codes for deep networks in PyTorch. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for NDEG:Grad Nondegree-CE-UIUC.
76213	Online Lecture	HFT	6:30PM -9:30PM	R	n.a.	Lariviere, D	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 3 hours Section Title: High Frequency Trading Section Info: Electronic Trading, informally known as “High Frequency Trading”, will teach students both the core concepts and underlying mechanics of, step by step, message by message, bit for bit, exactly how trillions of dollars in notional value are automatically traded daily around the globe. Electronic Trading will provide students with an exciting introduction both to the modern world of automated finance and to many exciting technologies that power it. Where does the “actual” real-time price of a particular asset come from at any point in time? How exactly is it being calculated and by who or what? Is there even a single price or are there multiple, and are any of those prices actually correct? How quickly can the price change or suddenly stop changing after plummeting? How do markets break or pricing models fail? Restriction(s): Not intended for students with Freshman class standing. Not intended for First Time Freshman students.