Learning objectives
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.
Prerequisites
Nobody
Course unit content
Knowledge engineering
-Solving Problems by Searching. Search Problems and Blind Search Techniques.
-Knowledge representation.
-First-Order Logic.Inference in First-Order Logic
-Description logic.
-Uncertain Knowledge and Reasoning
-Classical Planning
-Ontologies and metadata
-- XML and RDF.
--Taxonomies and Ontologies
- - Semantic Web, OWL.
Machine learning
Automatic learning and biological learning
Automatic learning in AI
Soft Computing techniques
- Neural networks
- Evolutionary Computation
- - Genetic Algorithms
- - Genetic Programming
Examples of applications
Full programme
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Bibliography
Course notes.
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
Teaching methods
Lectures and laboratory exercises
Assessment methods and criteria
Exercises carried on in the lab, written exam and final project
Other information
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