AE 598

Fall 2018 All Classes

All Classes

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Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in aerospace engineering intended to augment existing formal courses. Topics and prerequisites vary for each section. See Class Schedule or departmental course information for both.

May be repeated in the same or separate terms if topics vary to a maximum of 12 hours.

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AE 598 class schedule data for fall 2018
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
70952	Lecture	PE	1:00PM -2:50PM	TR	Everitt Laboratory	Putnam, Z	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Planetary Entry Section Info: Graduate-level overview of planetary entry systems. Topics include: flight mechanics; guidance, navigation, and control systems; thermal protection systems; aerothermodynamics; descent and landing systems; aerocapture; aerobraking; mission and vehicle design; and modeling and simulation. Case studies are used throughout to illustrate technical content. Pre-reqs: AE 312, AE 352, AE 402 (or equivalents) or permission from instructor
70953	Online	PEO	ARRANGED	n.a.	n.a.	Putnam, Z	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Section Title: Planetary Entry Section Info: Graduate-level overview of planetary entry systems. Topics include: flight mechanics; guidance, navigation, and control systems; thermal protection systems; aerothermodynamics; descent and landing systems; aerocapture; aerobraking; mission and vehicle design; and modeling and simulation. Case studies are used throughout to illustrate technical content. Pre-reqs: AE 312, AE 352, AE 402 (or equivalents) or permission from instructor Restriction(s): Restricted to MS:Industrial Engr Online-UIUC, MS: Civil Engr - Online - UIUC, MCS:Computer Sci Online -UIUC, MS:Mechanical Engineerng -UIUC, MS:Env Engr CivilEngr ONL-UIUC, MS: Aerospace Engr-Online-UIUC, NDEG:Grad Nondegree-CE-UIUC, or MENG:Mech Engineering Onl-UIUC.
70408	Lecture-Discussion	RL	10:00AM -11:50AM	TR	Lincoln Hall	Bretl, T West, M	Part of Term: 1 Date Range: 08/27/18-12/12/18 Section Title: Reinforcement Learning Section Info: Theory and practice of reinforcement learning as a tool for machine learning and artificial intelligence, applied to control, dynamics, and robotics, with a particular emphasis on computation. Topics will include reinforcement learning algorithms (temporal difference, Q-learning, policy gradient, actor-critic), function approximation and the use of deep neural networks, and efficient implementation on parallel architectures. Restrictions and prerequisites: CS 446 or equivalent; experience with TensorFlow, PyTorch, or equivalent.