Study-unit COMPUTATIONAL INTELLIGENCE
| Course name | Informatics |
|---|---|
| Study-unit Code | A002048 |
| Curriculum | Cybersecurity |
| Lecturer | Marco Baioletti |
| Lecturers |
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| Hours |
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| CFU | 6 |
| Course Regulation | Coorte 2023 |
| Supplied | 2024/25 |
| Supplied other course regulation | |
| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Sector | INF/01 |
| Type of study-unit | Opzionale (Optional) |
| Type of learning activities | Attività formativa monodisciplinare |
| Language of instruction | English |
| Contents | Taxonomy of optimization problems Evolutionary and swarm intelligence algorithms Probabilistic and fuzzy models in AI |
| Reference texts | Computational Intelligence: An Introduction. Andries P. Engelbrecht. Second Edition Wiley 2007 Introduction to Evolutionary Computing. A.E. Eiben, J.E. Smith. Second Edition Springer 2015 Probabilistic Graphical Models. Principles and Applications. Luis Enrique Sucar Springer 2015 |
| Educational objectives | The aim of this course is to acquire the main concepts of Computational Intelligence and the ability of applying them to various problems in Artificial Intelligence |
| Prerequisites | All knowledge required is covered by the undergraduate degree in Computer Science |
| Teaching methods | Theoretical frontal lessons Solutions of problems and cases study with the use of computers |
| Learning verification modality | The exam comprises two tests 1) a project to be developed as an individual homework. The purpose of this test is to check the ability to employ the knowledge acquired in the course 2) an oral test, where the student should present her/his project and discuss some theoretical topics seen in the course. The purpose of this test is to ascertain the knowledge level, understanding capabilities and communication skills acquired by the student. Students who do not speak Italian can do the exam in French or English. |
| Extended program | First part (Metaheuristic for Optimization problems) - Optimization problems - Exact methods - local search algorithms - tabu search - simulated annealing - genetic algorithms - evolutionary strategies - differential evolution - ant colony optimization - particle swarm optimization and other swarm intelligence algorithms - genetic programming - constrained optimization - multiobjective optimization Second part (Probabilistic and fuzzymodels) - uncertainty handling in AI - probabilistic models - Graphical models and bayesian networks - exact and approximate inference algorithms - bayesian network learning - extension of bayesian networks - Fuzzy sets - Fuzzy logic and reasoning - Fuzzy systems |