Difference between revisions of "SC:Soft Computing"

Latest revision as of 00:35, 25 March 2014

I am the Teaching assistant of the Soft Computing course, the official site of the course is not maintained by me and it can be found here. On this page I am publishing the material of my lectures for this class but you can find those also on the official course page.

Course Aim & Organization

Soft Computing includes technologies (Fuzzy Systems, Neural Networks, Stochastic Algorithms and models) to model complex systems and offers a powerful modeling tool for engineers and in general people needing to model phenomena. Among the application areas, we mention: data analysis, 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.

Teachers

The course is composed by a blending of lectures and exercises by the course teacher and the teaching assistant:

• Andrea Bonarini: the teacher
• Matteo Matteucci: the teaching assistant

Course Program

• 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 unsuprvised learning, main modelsi, selection and evaluation;
• Stochastic models: basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks;
• Applications: motivations, choices, models, case studies.