Intelligent Systems (ISs) are systems with the capability to sense the environment and react to it in order to attain given goals. Typically, intelligent systems have two main components: one focusing on learning the environment behavior on the basis of the acquired measurements (Machine Learning component) and another one focusing on planning the best course of actions given what has been learned so far (AI component).

Examples of intelligent systems are software controlling autonomous vehicles (e.g., cars, drones, a swarm of drones, satellites, vessels, robots, etc.); software controlling autonomous systems in general (e.g., bio-medical devices, trading robots, automatic video compression, decision support systems, etc.).

Intelligent systems are often mission-critical (i.e., their failure leads to a loss of money, e.g., a trading robot, a satellite, a swarm of drones) or safety-critical (i.e., their failure may lead to a loss of human lives, e.g., bio-medical devices, autonomous driving)

Designing and verifying intelligent systems is very challenging because of the wide set of operational scenarios (environment behaviors) they are supposed to withstand and the fact that the system behaviors changes (through learning) as a function of the inputs received.

The course has the following goals:

1) Learn how to model ISs, their environment, and their requirements using dynamical systems.

2) Learn basic techniques for the analysis and design of ISs using linear dynamical systems.

3) Learn how to use Montecarlo-based simulation, AI and Machine Learning techniques for the analysis and design of ISs.