Learning outcomes / competences

Students are able to create concepts for intelligent robot-based automation solutions. In doing so, they are able to take into account current communication concepts as well as learning algorithms. This enables students to realise partially or fully autonomous, stationary and mobile robots from the application spectrum of service robotics to industrial robotics.

Contents

    Introduction to
    Machine Learning
    Reinforcement learning
    Optimal control
    Probabilistic decision processes
    Probabilistic perception
    Fundamentals of probability theory
    Search and planning

Teaching methods

Lecture, exercise, seminar-style teaching, small group exercises on robots, project work

Prerequisites for participation

None

Forms of examination

Module examination in the form of a written examination (120 min.), presentation, homework or an oral examination

The lecture materials can be found in the institute'sMoodle learning rooms.