Chair of Computational Modeling and Simulation
TUM Department of Civil, Geo and Environmental Engineering
Technical University of Munich

Artificial Intelligence in Engineering

  • Master's elective course - summer semester - 2 SWS - 3 credits
  • Scheduled Thursdays 16:45 - 18:15 in room N0199

Content

  • Localization filters (Such as Bayesian filter, Kalman filter, particle filter)
  • Machine learning algorithms (Such as linear neural networks, Kohonen maps, reinforcement learning)
  • Expert systems (First order predicate logic & inference)
  • Robotics (Using real-life sensor data and actuators, practical application of localization filters)
  • Engineering applications of artificial intelligence

Objective

  • The students will gain an overview of artificial intelligence methods and their applications in the engineering field.
  • By finishing programming assignments using educational robots, the students learn to overcome practical obstacles when working with real life sensor data, actuators, and programming interfaces.

Study Material

  • The lectures will use power point slides and will be split into the different topics.
  • For each topic the connection to the fundamental origins (such as neural networks in human brains) will be presented as well as its connection to the engineering domain.
  • The students will learn to apply their knowledge on a practical localization problem using GoPiGo robot kits which will be provided to them.
  • The robot assignment will be performed in small teams of students.

Recommended Reading

  • Russell, S., Norvig, P., Canny, J., Malik, J., & Edwards, D. (1995). Artificial Intelligence: A Modern Approach. Prentice hall Englewood Cliffs.
  • Krishnamoorthy, C. S., & Rajeev, S. (1996). Artificial Intelligence and Expert Systems for Engineers. CRC Press LLC.

Prerequisites

  • Fundamental programming skills

Contact

Maximilian Bügler

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