After successful completion of the course, students are able to

• understand, apply and engineer large scale geographically distributed ML/AI applications

• understand communication mechanism for geographically distributed ML/AI applications with strict latency and/or data quality constraints

• understand the common AI/ML applications used for combating climate change (e.g., sensing of water pollution, flood sensing, etc.)

• facilitate computation and communication in rural and non inhabited areas

• understand how to apply different ML/AI methods for the implementation of applications combating climate change

• geographically distributed inference and learning in Ai/ML

The theoretical concepts are presented and discussed on the basis of slides and scientific literature. Practical
tasks are carried out in the laboratory on the basis of these concepts

The theoretical concepts are presented and discussed on the basis of slides and scientific literature. Practical tasks are carried out in the laboratory on the basis of these concepts

Oral exam at the end of the term