Artificial Intelligence (MSc)
People
Description
The aim of the course is to present the most modern techniques for solving complex problems. We focus on state of the art 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.
Objectives
Meta-Heuristics algorithms learning and testing
Teaching mode
In presence
Learning methods
Front lecture, text reading, code development For each topic you receive a paper to read and to support to your studies Simulated Annealing SA extension ILS: iterated local search Genetic Algorithms GA variant PBIL population-based incremental learning Ant Colony Optimization ACS for vehicle routing problems Tabu search
Examination information
Written test and code evaluation
Education
- Master of Science in Artificial Intelligence, Lecture, 2nd year
- Master of Science in Computational Science, Lecture, Elective, 1st year
- Master of Science in Computational Science, Lecture, Elective, 2nd year
- Master of Science in Informatics, Lecture, Artificial Intelligence, Elective, 1st year
- Master of Science in Informatics, Lecture, Artificial Intelligence, Elective, 2nd year
