Year of erogation: 
Disciplinary Sector: 
Computer Science
Second semester
Year of study: 
Language of instruction: 


Learning outcomes of the course unit

The aim of the course is to introduce various aspects related to the foundations of Artificial Intelligence.
With reference to Dublin indicators:
Knowledge and understanding
During the course, the main ideas related to Artificial Intelligence are introduced. Students are encouraged to study and elaborate on the topics discussed during the course.
Applying knowledge and understanding
Acquired theoretical knowledge is applied to solve specific problems. During the course, some exercise sessions are dedicated to the solutions of specific problems.
Making judgments
Exercises proposed during classes can be solved individually or in groups, and they often can be solved in different ways. Students can compare their approach to the solutions proposed by other students and to the solutions shown during classes. Such comparisons enhance the development of specific skills that are useful to better understand the considered problems.
Communication skills
Discussions during classes allow students to improve their communications skills. Such discussions concern specific techniques to solve proposed problems and they focus on advantages and disadvantages of the proposed approaches. Students learn to work individually and in groups.
Learning skills
The study of different aspects related to Artificial Intelligence and its applications helps students to improve in-depth comprehension of the topics. Acquired knowledge can be adapted to solve specific problems that may be different from those discussed during classes. Students acquire useful techniques to work in groups and autonomously.


Basic notions of university-level calculus and programming.

Course contents summary

The foundations of Artificial Intelligence are discussed during the course. Particular focus is given to the study of the mathematical foundations of Artificial Intelligence. In particular, the following topics are discussed in the course:
- Introduction to the mathematical foundations of computer vision and image processing
- Introduction to the mathematical foundations of neural networks and machine learning
- Introduction to game theory and to the mathematical foundations of decision problems
- Introduction to constraint satisfaction problems and, in particular, to polynomial constraints
- Introduction to the mathematical foundations of natural language processing

Recommended readings

Stuart Russell e Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson Education, 2016.

Teaching methods


Assessment methods and criteria

The final exam consists in a written test concerning all the topics discussed during the course.