Unit COMPUTATIONAL INTELLIGENCE

Course
Informatics
Study-unit Code
A002048
Curriculum
Cybersecurity
Teacher
Marco Baioletti
Teachers
  • Marco Baioletti
Hours
  • 52 ore - Marco Baioletti
CFU
6
Course Regulation
Coorte 2020
Offered
2021/22
Learning activities
Affine/integrativa
Area
Attività formative affini o integrative
Academic discipline
INF/01
Type of study-unit
Opzionale (Optional)
Type of learning activities
Attività formativa monodisciplinare
Language of instruction
English
Contents
Evolutionary and swarm intelligence algorithms

Probabilistic models in AI

Fuzzy logic and systems
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 and
exercises 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 (Evolutionary Computation and Swarm Intelligence)
- Optimization methods and local search algorithms
- simulated annealing
- genetic algorithms
- evolutionary strategies
- differential evolution
- ant colony optimization
- particle swarm optimization and other swarm intelligence algorithms
- genetic programming

Second part (Probabilistic models)
- uncertainty handling in AI
- probabilistic models
- Graphical models and bayesian networks
- exact and approximate inference algorithms
- bayesian network learning
- random field
- Dynamic and temporal bayesian networks
- hidden markov models
- relational probabilistic models

Third part (fuzzy logic and systems)
- Fuzzy sets
- Fuzzy logic and reasoning
- Fuzzy systems
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