Learning outcomes of the course unit
The course aims to introduce students to the techniques and technologies designed to reproduce smart behaviours on the computer, typical of living beings, with particular attention to the knowledge engineering and machine learning techniques.
Course contents summary
-Solving Problems by Searching. Search Problems and Blind Search Techniques.
-First-Order Logic.Inference in First-Order Logic
-Uncertain Knowledge and Reasoning
-Ontologies and metadata
-- XML and RDF.
--Taxonomies and Ontologies
- - Semantic Web, OWL.
Automatic learning and biological learning
Automatic learning in AI
Soft Computing techniques
- Neural networks
- Evolutionary Computation
- - Genetic Algorithms
- - Genetic Programming
Examples of applications
Russell, Norvig - Intelligenza Artificiale: un approccio moderno 2/Ed, Prentice Hall, 2005
Haykin - Neural networks. US Imports & PHIPEs, 1999
Engelbrecht - Computational Intelligence: an introduction, 2a edizione, Wiley, 2007
Eiben - Smith, Introduction to Evolutionary Computing, Springer, 2003
Lectures and laboratory exercises
Assessment methods and criteria
Exercises carried on in the lab, written exam and final project