- Theoretical background for the design of cyber-physical systems (CPS). Models and algorithms to state estimation, control and planning are introduced.
- Usage of tought paradigms, i.e., implementation of parts of a CPS including an introduction to robotics.
- Supervised learning with Deep Neural Networks.
- 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.
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
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.
<p>Homework/project assignments and oral examination.</p>
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