Goals

Soft Computing includes technologies (Fuzzy Systems, Neural Networks, Deep learning, Stochastic Algorithms and models) to model complex systems and offers powerful modeling tools for engineers and in general people needing to model complex phenomena. Among the application areas, we mention: (big) data analysis, classification, automatic control, modeling of artificial and natural phenomena, modeling of behaviors (e.g., of users and devices), decision support. The course will introduce rigorously the fundamentals of the different modeling approaches, will put in evidence the application possibilities, by comparing different models, examples and application cases, will introduce design techniques for systems based on these technologies. No specific background is required.

Contents

What is Soft Computing: fuzzy systems, neural networks, stochastic algorithms and models - Fuzzy models: fuzzy sets, fuzzy logic, fuzzy rules, motivations for fuzzy modeling -Neural networks: basics, supervised and unsupervised learning, main models, deep learning, selection and evaluation. Stochastic models: basics, evolutionary computation, fitness function, model definition, genetic algorithms.  Applications: motivations, choices, models, case studies.

The evaluation consists in a written exam where both theoretical competence and modeling skills will be tested. Frequence to lessons is important mainly to develop these last.

Students are strongly advised to consider ALL the resources, included books and not only the slides.