After successful completion of the course, students are able to master the following competences.

Fachliche und methodische Kompetenzen: Stochastic foundations of cyber-physical systems, artificial intelligence, and robotics. 

 Kognitive und praktische Kompetenzen:

• Ability to learn stochastic models of CPS. 
• Ability to perform stochastic analysis of CPS 
• Ability to design optimal controllers for CPS. 

Soziale Kompetenzen und Selbstkompetenzen: Apprehension of and experience in applying theory for solving practical problems.

• Probabilistic interpretation of uncertainty.
• Rational agents as smart cyber-physical systems (CPS).
• Static (sBN) and dynamic (dBN) Bayesian networks (BN).
• Uncertain environments as sBN and dBN.
• Exact and approximate inference in BN.
• Machine learning (supervised) of sBN and dBN.
• Decision making and optimal control for Markov Decision Processes.
• Supervised (sML) and reinforcement (rML) learning.
• Machine learning (sML and rML) with deep neural networks.
• Speech-recognition and robotics.

Didactic concept: Topics are tought in the lectures and practiced in exercises including programming exercises, simulation and application on real-world mobile robots.

Weekly lecture with continually accompanying home-work assignments, deepening the understanding of the module content and increasing the individual problem-solving competence in CPS modelling, analysis, and control. Hand-written or Latex solutions, possibly their mutual peer reviewing, accompanying reading of a book

Lectures start s.t.

ECTS-Breakdown 3 ECTS = 150 hours:

Lecture part:

• 0.5h  lecture introduction
• 54h (18 lectures, 2h per lecture + 1h pre/postprocessing)
• 20h exam preparation
• 0.5h  oral exam
  ----
  75h

Exercise part:

• 75h exercises

Homework/project assignments and oral examination.

Registration to the course via TISS. You will be added to the TUWEL course, where the rest of the course will be organized.

S. Russel and P. Norvig, Artificial Intelligence - A Modern Approach, 3rd ed., Upper Saddle River, New Jersey: Pearson Education, 2010.

R.S. Sutton and A.G. Barto - Reinforcement Learning An Introduction second edition. The MIT Press Cambridge, Massachusetts London,  England, 2018.

Mandatory prerequisites: None. The following prerequisites are helpful but not mandatory.

Fachliche und methodische Kompetenzen: Probability theory, stochastic signals, control theory, discrete mathematics.

Kognitive und praktische Kompetenzen: Mathematical reasoning and implementation skills. 

Soziale Kompetenzen und Selbstkompetenzen: Independent work, interest in combining theory and practice. 

These prerequisites are provided in the following modules: Wahrscheinlichkeitstheorie und Stochastische Prozesse, Signale und Systeme, Modellbildung und Regelungstechnik, Discrete Mathematics