Short description

The aim of the course is to provide an introduction to the topic of uncertainty modeling in computer science,
with specific reference to artificial intelligence (knowledge representation and machine learning). To this aim,
the course will provide the basic conceptual tools to understand and recognize different types of uncertainty,
as well as the mathematical and computational tools underlying state-of-the-art uncertainty modeling methods.

Program (tentative)

• Knowledge Representation
  . Information and uncertainty: relationships and taxonomies
  . Tools for modeling uncertainty: probability theory, possibility theory, belief functions, fuzzy sets, rough sets
• Machine Learning
  . Uncertainty in ML: basic definitions
  . Uncertainty in the data: weakly supervised learning and learning from imprecise data
  . Uncertainty quantification in supervised learning