AE 598

Spring 2024 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 spring 2024
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
42106	Laboratory-Discussion	3DV	11:00AM -12:20PM	TR	1038 Campus Instructional Facility	Bretl, T	Part of Term: 1 Date Range: 01/16/24-05/01/24 Credit: 4 hours Section Title: An Invitation to 3-D Vision Section Info: AE 598 3DV: An invitation to 3-D vision This course provides a rigorous and hands-on introduction to three-dimensional vision - that is, to the reconstruction of 3D models from 2D images. Students with a solid background in linear algebra and a working knowledge of the python programming language will learn both fundamental theory (image formation, feature detection and matching, multiple-view geometry, and nonlinear optimization) and methods of computation (both from scratch and with off-the-shelf code). No prior exposure to computer vision is required.
56934	Lecture-Discussion	AAO	9:30AM -10:50AM	TR	410B1 Engineering Hall	Ornik, M	Part of Term: 1 Date Range: 01/16/24-05/01/24 Credit: 4 hours Section Title: Autonomy Against the Odds Section Info: Topic: Autonomy Against the Odds: Stochastic Control for Motion and Mission Planning Stochastic control processes are often used to describe system behavior in complex, unexplored, or changing environments – the future playing fields of autonomous systems. However, performing even the simplest classical control tasks in such a framework requires the development of new theories of dynamical systems, optimal control, reachability, and adaptation. This course will serve as a gentle first introduction to that endeavor. Topics covered will include: basics of formal probability and measure theory, Markov chains, Markov decision processes, dynamic programming, optimal policies, partial observability and beliefs, robust control for uncertain processes, exploration-exploitation tradeoff and value of information, multi-agent planning, and automata and product spaces. Prerequisite: familiarity with graph theory, probability, and random variables
69582	Online	AOL	ARRANGED	n.a.	n.a.	Ornik, M	Part of Term: 1 Date Range: 01/16/24-05/01/24 Credit: 4 hours Section Title: Autonomy Against the Odds Section Info: Topic: Autonomy Against the Odds: Stochastic Control for Motion and Mission Planning Stochastic control processes are often used to describe system behavior in complex, unexplored, or changing environments – the future playing fields of autonomous systems. However, performing even the simplest classical control tasks in such a framework requires the development of new theories of dynamical systems, optimal control, reachability, and adaptation. This course will serve as a gentle first introduction to that endeavor. Topics covered will include: basics of formal probability and measure theory, Markov chains, Markov decision processes, dynamic programming, optimal policies, partial observability and beliefs, robust control for uncertain processes, exploration-exploitation tradeoff and value of information, multi-agent planning, and automata and product spaces. Prerequisite: familiarity with graph theory, probability, and random variables Restricted to online grad non-degree, online MCS, online MSME, online MSCEE, and online MSAE students. For more details on this course section, please see http://engineering.illinois.edu/online/courses/. Non-Degree students may enroll on a space-available basis with consent of Jenna Russell (jennar@illinois.edu). Restriction(s): Restricted to MS: Civil Engr - Online - UIUC, MS:Industrial Engr Online-UIUC, MS:Mechanical Engineerng -UIUC, MS:Env Engr CivilEngr ONL-UIUC, NDEG:Engineering GR ONL - UIUC, MS: Aerospace Engr-Online-UIUC, MENG:Engr:Energy Sys Onl-UIUC, MENG:Mech Engineering Onl-UIUC, MENG:Elec & Comp Eng ONL -UIUC, MENG:Engr:AeroSys Online- UIUC, or MENG:ENGR:Digital Ag ONL- UIUC.