Teaching
The list of courses taught by members of the Computational Intelligence Group.
| Information systems | Intelligent systems | Soft computing |
Courses
Information systems
Laurea Specialistica in "Ingegneria Informatica per la Gestione d'Azienda"
Laurea Specialistica Degree in "Information Technology Engineering for Business Management"
Facoltà di Ingegneria, Università di Pisa
- Software development process: Software life cycle, waterfall, spiral and iterative models. The Unified Process. The UML language: Class, Object, Statechart, Activity, Sequence, Collaboration, Use-case, Component, Deployment diagrams. Model and Meta-model. Experiences with UML tools designed for building large scale software systems.
- Business modeling: Business processes: resources, goals, business rules. Common business views: business vision, business process, business structure, business behavior. Defining business rules by Object Contraint Language. Business patterns to model business processes, resources, goals and rules. From business architecture to software architecture: using the business architecture to define the software architecture of information systems.
Intelligent systems
Laurea Specialistica in "Ingegneria Informatica per la Gestione d'Azienda"
Laurea Specialistica in "Ingegneria Informatica"
Laurea Specialistica Degree in "Information Technology Engineering for Business Management"
Laurea Specialistica Degree in "Information Technology Engineering"
Facoltà di Ingegneria, Università di Pisa
- Expert systems and Decision support systems. Basic concepts of expert systems. Knowledge representation. Knowledge engineering. Inference. Tools for building expert systems. Basic concepts of decision support systems.
- Artificial neural networks. Fundamentals of artificial neural networks. Neural network training. Backpropagation learning. Pattern classification. Self-organizing feature maps. Clustering.
- Fuzzy sets and fuzzy logic. Fundamentals of fuzzy sets and fuzzy logic. Fuzzy relations. Linguistic variables. Fuzzy rule systems. Approximate reasoning.
- Genetic algorithms. The canonical genetic algorithm. Selection operators. Crossover and mutation operators. Real-valued encoding.
- Data mining. Data mining techniques. Data mining applications: classification and prediction.
Soft computing
Doctoral Program in "Computer Science and Engineering"
IMT Lucca Institute for Advanced Studies
Soft computing is a collection of methodologies, including Fuzzy Logic, Artificial Neural Networks and Genetic Algorithms, that tolerate imprecision, uncertainty and approximation. The course will introduce approximate reasoning, fuzzy rule systems, the main neural network models, and genetic algorithms. The major application areas of soft computing will be presented, such as decision support, pattern classification and recognition, and time series prediction.
