Course Description

In this course we introduce concepts such as Bayesian Networks to model uncertainty and knowledge of the world in a natural, efficient, and systematic way.

Content

• Handling Uncertainty
• Probabilistic Reasoning
• Probabilistic Reasoning over Time
• Learning Bayesian Networks
• Making Simple Decisions
• Making Complex Decisions

Goals

• Professional competency:
  • The students know probabilistic models like Bayesian Networks, Gaussian and other probability distributions to describe uncertain processes.
• Methodological competency:
  • Ability to apply probabilistic methods like Kalman Filters or Particle Filters to data processing, modeling and reasoning tasks.
• Individual competency:
  • Sensibility for strength and limits of the significance and validity of probabilistic descriptions and probability based arguments.