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Artificial Intelligence

Description

 

COURSE OBJECTIVES
Meta-Heuristics algorithms learning and testing

 

COURSE DESCRIPTION
The aim of the course is to present the most modern techniques for solving complex problems. We focus on meta-heuristic for continuous function and combinatorial optimization: among the methods we deepen simulated annealing, genetic algorithms, variable neighborhood search and ant colony optimization. Gaming with two players will be also discussed as the integration between meta-heuristics and machine learning. Students are asked to implement and test some of these techniques.

 

LEARNING METHODS
Front lecture, text reading, code development

 

EXAMINATION INFORMATION
Mid terms, Final written exam

 

REFERENCES
Required Materials

  • Artificial Intelligence: A Modern Approach (3rd Edition) by Stuart Russell, Peter Norvig
  • Artificial Intelligence, third edition, P.H. Winston, Addison-Wesley
  • Genetic Algorithms in Search, Optimisation, and Machine Learning, Goldberg, Addison-Wesley, MA
  • Ant Colony Optimization By Marco Dorigo and Thomas Stützle A Bradford Book Course
  • Material in English will be provided to the students

People

 

Gambardella L. M.

Course director

Mele U. J.

Assistant

Additional information

Semester
Fall
Academic year
2020-2021
ECTS
6
Language
English
Education
Master of Science in Artificial Intelligence, Core course, Lecture, 2nd year