Foundations of Reinforced Learning for Engineers: An Engineering Management Perspective

#Core #mathematical #concepts; #Markov #Decision #Processes; #Technical #fundamentals; #Reinforcement #Learning; #Engineering #Managers
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Technology and Engineering Management (TEM) chapter is pleased to organize a Two-Part Series titled, “Reinforcement Learning for Engineers” with an Engineering Management Prespective.

 We are pleased to invite you to the first of the two-part learning series on Reinforcement Learning (RL). While each session can be attended independently, we highly recommend REGISTERING for BOTH MEETING/ SESSIONS and participating at NO-COST in both sessions to gain a complete, progressive understanding of the topic.

Meeting/Session 1: Foundations of Reinforcement Learning for Engineers: An Engineering Management Prespective

  •  Date: Thursday, October 15
  •  Focus: Core mathematical concepts, Markov Decision Processes, and technical fundamentals.

Meeting/Session 2: Reinforcement Learning for Engineers : An Engineering Management Prespective

  • Date: Thursday, October 29
  • Focus: Advanced implementation strategies, real-world engineering applications, and system deployment.

  Date and Time

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  • Starts 21 September 2026 08:55 PM UTC
  • Ends 15 October 2026 11:59 PM UTC
  • No Admission Charge

  Speakers

Electrical and Computer Engineering, University of Central Florida

Topic:

Meeting/Session 1: Foundations of Reinforcement Learning for Engineers

Meeting/Session 1:

Foundations of Reinforcement Learning for Engineers: An Engineering Management Perspective

Biography:

Precious Nwaorgu is a Ph.D. research scholar in Electrical and Computer Engineering at the University of Central Florida. His research focuses on reinforcement learning, agent generalization, safety constraints, and knowledge transfer for autonomous decision-making. His work studies how intelligent agents can make reliable decisions when tasks, dynamics, and operating conditions change.




Agenda

Will introduce and clarify

Core mathematical concepts

Markov Decision Processes and

Technical fundamentals necessary to understand Reinforcement Learning