Difference between revisions of "SC:Soft Computing"
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* '''Applications''': motivations, choices, models, case studies. | * '''Applications''': motivations, choices, models, case studies. | ||
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A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (they will be notified to you by email). | A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (they will be notified to you by email). | ||
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* Follow this link to [[Media:Risultati_SC.pdf | the results of 14/07/2011 exam]]. | * Follow this link to [[Media:Risultati_SC.pdf | the results of 14/07/2011 exam]]. | ||
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Revision as of 08:41, 9 October 2012
The following are last minute news you should be aware of ;-)
06/10/2011: the Soft Computing course starts today!
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.
