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Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2016/2017
Tipo incarico Dottorato
Insegnamento 096701 - SOFT COMPUTING THEORY, TECHNIQUES, AND APPLICATIONS
Docente Bonarini Andrea
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Dottorato Da (compreso) A (escluso) Insegnamento
MI (1300) - SCUOLA DI DOTTORATOAZZZZ096701 - SOFT COMPUTING THEORY, TECHNIQUES, AND APPLICATIONS

Programma dettagliato e risultati di apprendimento attesi

Goals

 

Introduction to fundamental theories and techniques of Soft Computing: Fuzzy Systems, Neural Networks, Stochastic Algorithms (e. g., Genetic Algorithms and Evolutionary Algorithms). Presentation of case studies and analysis of application possibilities.

 

 

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, selection and evaluation. Stochastic models: basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning.  Applications: motivations, choices, models, case studies.

 

Subject

 

Soft Computing includes technologies (Fuzzy Systems, Neural Networks, Stochastic Algorithms , Bayesian Networks, ...) to model complex systems and offers a powerful tool both for research and companies in different, rapidly growing application areas, such as, for instance: data analysis, automatic control, modelling of artificial and natural phenomena, modelling of behaviours (e.g., of users and devices), decision support. The course will introduce rigorously the fundamentals of the different modelling 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.

 


Note Sulla Modalità di valutazione

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.


Intervallo di svolgimento dell'attività didattica
Data inizio
Data termine

Calendario testuale dell'attività didattica

Lessons will be offered in the Seminar Room at  DEIB, Building 20, Via Ponzio 34/5

February 7th h. 14-18

February 8th h. 9-13

February 9th h. 9-13

February 10th h. 9-13

February 16th h. 9-13

February 17th h. 9-13


Bibliografia
Risorsa bibliografica obbligatoriaSlides, links to free material, book suggestions are provided through the course web page on BEEP https://beep.metid.polimi.it
Note:

Students are strongly adviced to ocnsider ALL the resources, included books and not only the slides.


Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
24.0
esercitazione
0.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese

Note Docente
schedaincarico v. 1.6.2 / 1.6.2
Area Servizi ICT
04/06/2020