In this subfolder you find all tutorials specifically provided to support the understanding and simplify the use of the produced software packages.
With the provided code, which is based on agentMET4FOF, we showcase an agent-based machine learning approach for online anomaly detection of (in our case simulated) sensor readings.
This is an implementation of the agent-based approach for the ZEMA dataset 
This is an implementation of an agent-based approach to machine learning utilizing the external Python library scikit-multiflow.
This is an implementation of an agent-based approach to interconnect hardware sensors from the manufacturer Seneca and processing the produced data streams including sensor data buffering as part of the agents' implementation.
This is an implementation of the agent-based approach for the ZEMA dataset
This code serves to connect the SmartUp Unit developed in WP1 to agentMET4FOF.
We prepared a collection of tutorials and examples to document, explain and illustrate the possibilities offered by PyDynamic.
Machine Learning tutorials oriented at beginners in data science. Methods are applied on Strathcylde-s testbed data (Advanced Forming Research Centre | University of Strathclyde).
These notebooks were developed on the basis of previous work on Machine Learning methods applied on ZeMA's testbed data of the co-author Haris Lulic .
The collected code originates from GitHub repositories which are of course directly accessible as well. The respective links are the following: