Unit COMPUTATIONAL INTELLIGENCE
- Course
- Informatics
- Study-unit Code
- A002048
- Curriculum
- Artificial intelligence
- Teacher
- Marco Baioletti
- Teachers
-
- Marco Baioletti
- Hours
- 52 ore - Marco Baioletti
- CFU
- 6
- Course Regulation
- Coorte 2023
- Offered
- 2023/24
- Learning activities
- Caratterizzante
- Area
- Discipline informatiche
- Academic discipline
- INF/01
- Type of study-unit
- Obbligatorio (Required)
- 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
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