The course focuses on contents that allow to understand the theoretical bases and techniques of Artificial Intelligence, providing fundamentals of Computer Science, Mathematics, Physics, Statistics, Cognitive Sciences and also of Law and Ethics, which is essential for understanding the constraints that limit the socially acceptable use of these new tools.

Check the program of each course on the course catalogue

Teaching Semester
Computational logic 1st
Experimental physics for AI 1st
Computer programming, algorithms and data structures 1st and 2nd
Knowledge representation and reasoning 1st and 2nd
Calculus 1st and 2nd
Theoretical and computational linear algebra 2nd
Cognitive psychology 2nd

Teaching Semester
Ethic, law and AI 1st
Machine learning, artificial neural networks and deep learning 1st and 2nd
Probability and statistical inference 1st and 2nd
Theoretical and quantum Physics for AI 1st and 2nd
Fuzzy systems and evolutionary computing 2nd
Text mining and natural language processing 2nd

Teaching Semester
Statistical modelling 1st
Brain modelling 2nd
Chosen Track courses (see below) –
Electives (⨯2) –
Stage (⨯1) or laboratories (⨯3) –
Final exam –

During the third year, students can choose one of the following 4 tracks containing four courses each:

  • Data mining and knowledge extraction
  • Information retrieval and recommender systems
  • Web and social networks search and analysis
  • Artificial intelligence for communication and marketing

  • Signal and image processing
  • Process control, industrial automation and robotics
  • Medical applications and health-care
  • Human-system interaction

  • Logics for practical reasoning and AI
  • Brain-inspired neural networks and neural architectures
  • Human system interaction
  • AI and society

  • Experimental physics 2
  • Imaging and spectroscopy for environment and health (Lab.)
  • Materials and platforms for AI
  • Mathematics for imaging and signal processing