This course aims at providing students with a unifying overview about modern artificial intelligence. First of all symbolic artificial intelligence is introduced, along with the depth-first and breadth-first exploration methods. Then the concept of agent is covered, together with the intro-duction of multiple agent systems as a unifying model of many distributed AI systems regularly used today. In particular, swarm and evolutionary intelligence are two paradigms based on multi-agent systems, which have proved to be very effective in solving applications that require a dis-tributed approach. The final part of the course is devoted to advanced topics in artificial intelli-gence, such as: how to speed up deep neuro-fuzzy networks (using novel representations for real numbers and implementing the associated hardware accelerators), design and validation of one-class classifiers, the design of neural networks with infinitesimal or infinite weights, etc.