|MODULBEAUFTRAGTE:R:||Prof. Dr. Gregor Schöner|
|DOZENT:IN:||Prof. Dr. Gregor Schöner|
|FAKULTÄT:||Fakultät für Informatik|
|ANGEBOTEN IM:||jedes Sommersemester|
This course is given with the flipped/inverted classroom concept. The
students work through online material beforehand and this will then be deepened in the contact sessions,
which will be used for an interactive exchange between students and with the lecturer in a flexible format.
LERNZIELEAfter successful completion of this course, students will be able to
* summarize a number of fundamental methods in artificial intelligence,
* explain their mathematical basis and algorithmic nature,
* apply them to simple problems,
* decide which methods are suitable for which problems, and
* communicate about the all that in English.
INHALTThis course gives an overview over representative methods in artificial intelligence:
formal logic and reasoning, classical methods of AI, probabilistic reasoning, machine learning, deep neural
networks, computational neuroscience, neural dynamics, perception, natural language processing, robotics.
VORAUSSETZUNGEN CREDITSBestandene Modulabschlussprüfung
EMPFOHLENE VORKENNTNISSEBasic knowledge of calculus and linear algebra.