|SCIENTIFIC DISCIPLINARY SECTOR||INF/01|
Computational Intelligence constitutes a repertoire of Artificial Intelligence predictive methodologies build on data and on domain knowledge, which are part of the background of the strategic engineer.
Neural networks; fuzzy logic systems; evolutionary computing; swarm intelligence; neuro-fuzzy and fuzzy neural systems; hybrid intelligent systems, machine learning; classification, regression learning, clustering
The course presents a systematic introduction to the foundations and the applications of Computational Intelligence models which are advanced data processing methods of Artificial Intelligence inspired by natural systems and that encompass artificial neural networks, fuzzy logic systems, evolutionary calculus, swarm intelligence and machine learning. The most relevant topics, such as classification and regression, will be addressed both from a theoretical point of view and through practical programming exercises and homework using the Python language.
The Course does not require specific prerequisites and includes all the necessary elements and references. The basic knowledge in mathematics, statistics acquired in previous studies, and programming skills in Python will be useful for improving the learning curve and student performance.
1 Lecture of 4 hours in a row per week for 10 weeks including frontal lectures, Class exercises and home-works.
Optimization; Machine Learning; Regression; Classification; Bayesian Decision Theory; Parametric Classification; Intro to clustering; Fuzzy Sets; Fuzzy Clustering; Kernel Clustering; Spectral Clustering; Networks' Analysis; Neural Networks; Support Vector Machines; Multi-Layer Perceptrons; Fuzzy Systems; Deep Learning; Ensembles; Genetic Algorithms; Evolution Strategies; Particle Swarm Optimization; Multi-Objective Genetic Algorithms; Multimodal Medical Volumes Segmentation; Seminars by companies operating in AI; Demos; Homeworks.
• Textbook: Andries P. Engelbrecht: Computational Intelligence - An introduction, Wiley, 2007.
• Selection of relevant journal papers
• Lecture notes / slides
Office hours: On Thursday from 16.00 to 18.00 by appointment agreed on email (The teacher has more courses for various courses of study, always specify the name and course)
FRANCESCO MASULLI (President)
STEFANO ROVETTA (President Substitute)
All class schedules are posted on the EasyAcademy portal.
Homeworks and oral exam