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.