SE 498

Spring 2022 All Classes

All Classes

Credit: 0 TO 4 hours.

Subject offerings of new and developing areas of knowledge in Systems Engineering and Design 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.

Section Status updates every 10 minutes.
SE 498 class schedule data for spring 2022
CRN Type Section Time Day Location Instructor Section Details
73163
Lecture
DP
2:00PM -3:20PM
TR
101 Transportation Building
Shamma, J
Part of Term:
1
Date Range:
01/18/22-05/04/22
Credit:
3 hours
Section Title:
Dynamic Prog Reinforce Learn
Section Info:
Prerequisites: IE 300, IE 310, and MATH 257 or Math 415 or equivalents. Course Description: This course is an introduction to dynamic programming, which is a framework for deriving optimal decision policies in dynamic and uncertain environments. Topics include the principle of optimality in deterministic and stochastic settings, value and policy iteration, and imperfect state measurement problems. The course continues with coverage of simulation-based methods and online reinforcement learning for approximate dynamic programming in computationally challenging settings.
Restriction(s):
Not intended for students with Freshman, Sophomore, or Junior class standing.
73693
Lecture
DS
2:00PM -3:20PM
TR
101 Transportation Building
Shamma, J
Part of Term:
1
Date Range:
01/18/22-05/04/22
Credit:
4 hours
Section Title:
Dynamic Prog Reinforce Learn
Section Info:
Prerequisites: IE 300, IE 310, and MATH 257 or Math 415 or equivalents. Course Description: This course is an introduction to dynamic programming, which is a framework for deriving optimal decision policies in dynamic and uncertain environments. Topics include the principle of optimality in deterministic and stochastic settings, value and policy iteration, and imperfect state measurement problems. The course continues with coverage of simulation-based methods and online reinforcement learning for approximate dynamic programming in computationally challenging settings.
Restriction(s):
Restricted to Graduate - Urbana-Champaign.
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