


Introduction to Machine Learning
This lecture will cover the basic concepts of Machine Learning to alleviate inconsistencies towards concept and notation accuracy. Supervised, self-supervised, unsupervised, semi-supervised learning. Multi-task Machine Learning.



Agent Systems
This lecture overviews Agent Systems that has many applications in multi-party behavior modeling.



Natural Selection
This lecture overviews Natural Selection simulation that has many applications in ecology and sociology studies.



Robot and Drone Swarms
This lecture overviews Robot and Drone Swarms that has many applications in autonomous systems: cars/drones.



Self-Awareness in Autonomous Systems
This lecture overviews Self-Awareness that has many applications in in Autonomous Systems and robotics. It covers the following topics in detail: Self-awareness definition, Self-aware systems, Cognitive architecture.

Position paper on AI for the operation of critical energy and mobility network infrastructures
This position paper outlines AI4REALNET’s approach to applying AI in network infrastructure operations, translating application needs into algorithmic proposals for effective human-AI collaboration in decision-making processes.