Inhalt

[ 993TAMR19 ] Studienfach (*)AI and Mechatronics – Robotics and Autonomous Systems

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Form der Prüfung Ausbildungslevel Studienfachbereich VerantwortlicheR Anbietende Uni
16,5 ECTS Kumulative Fachprüfung M1 - Master 1. Jahr Informatik Sepp Hochreiter Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Artificial Intelligence 2019W
Ziele (*)In this elective track, students learn to apply AI techniques to robotics and autonomous systems. Robotics deals with the construction and operation of robots as well as computer systems for their control, sensory feedback, and information processing. Autonomous systems include self-driving cars, autonomous drones, and production systems that work independent of human control. Robots and other autonomous machines are steered by control techniques such as adaptive optimal control known as reinforcement learning in the field of AI. AI methods concerning the perception and model-building of the environment, planning, and self-localization are crucial for robotics and autonomous systems. These techniques can be complemented by learning a model from data in contrast to classical techniques in mechatronics.
Lehrinhalte (*)The contents of this subject result from the contents of its courses.
Untergeordnete Studienfächer, Module und Lehrveranstaltungen