AI systems are increasingly integrated into our everyday lives. These are, for example, systems that can understand and generate language or analyze images and videos. In addition, AI systems are essential in robotics in order to be able to develop the next generation of intelligent robots.

Based on the knowledge of the lecture “Einführung in der KI”, the students learn to understand, develop and evaluate these systems. In order to bring this knowledge closer to the students, the lecture is divided into 4 parts. First, the methods of perception using different modalities are treated. The second part deals with advanced methods of learning that go beyond supervised learning. Then methods are discussed that are required for the representation of knowledge in AI systems . Finally, methods are presented that enable AI systems to generate content.

Requirements:

None

Recommendations:

- “Einführung in der KI”

- Good basic knowledge of mathematics

Workload :

approx. 180 hours, of which

approx. 45 hours lecture attendance

approx. 15 hours exercise visit

approx. 90 hours post-processing and processing of the exercise sheets

approx. 30 hours exam preparation

Learning goals:

• The students know the relevant elements of a technical cognitive system and their tasks.
• The students understand the algorithms and methods of AI to model technically cognitive systems.
• The students are able to understand the different sub -components to develop and analyze a system .
• The students can transfer this knowledge to new applications, as well as analyze and compare different methods.

success control:

See the module manual!