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Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2018/2019
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 052536 - SOFT COMPUTING
Docente Bonarini Andrea
Cfu 5.00 Tipo insegnamento Monodisciplinare
Didattica innovativa L'insegnamento prevede  1.5  CFU erogati con Didattica Innovativa come segue:
  • Blended Learning & Flipped Classroom

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing - Civ (Mag.)(ord. 270) - MI (495) GEOINFORMATICS ENGINEERING - INGEGNERIA GEOINFORMATICA*AZZZZ052536 - SOFT COMPUTING
Ing Ind - Inf (Mag.)(ord. 270) - MI (263) MUSIC AND ACOUSTIC ENGINEERING*AZZZZ052536 - SOFT COMPUTING
Ing Ind - Inf (Mag.)(ord. 270) - MI (471) BIOMEDICAL ENGINEERING - INGEGNERIA BIOMEDICA*AZZZZ052536 - SOFT COMPUTING
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA*AZZZZ052536 - SOFT COMPUTING

Obiettivi dell'insegnamento

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.

Goals for this course are listed in the following.

- Presentation of basic knowledge about some technologies within the Soft Computing area, namely: Fuzzy Systems, Neural Networks, Deep learning, Stochastic Algorithms and models.

- Presentation of tools to implement the mentioned technologies.

- Analysis of paradigmatic case studies to understand the applicability issues of the mentioned technologies.

- Development of the ability to analyze a problem, to select the appropriate technology for a problem, to design data, architectures and processes for the mentioned technologies

- Development of the ability to learn autonomously both declarative and procedural knowledge (thanks to innovative teaching methods, such as flipped class, and blended learning)

 


Risultati di apprendimento attesi

Acquisition of basic knowledge about some technologies within the Soft Computing area, namely: Fuzzy Systems, Neural Networks, Deep learning, Stochastic Algorithms and models. (DD1)

Acquisition of the ability to analyze a problem, to select the appropriate technology for a problem, to design data, architectures and processes for the mentioned technologies (DD2, DD3)

Acquisition of basic operational abilities to implement the mentioned technologies. (DD2)

Acquisition of the ability to present both the knowledge, the process, and the proposed solutions, as well as to analyze results and data (DD4)

Acquisition of the ability to learn autonomously both declarative and procedural knowledge.


Argomenti trattati
  • What is Soft Computing: fuzzy systems, neural networks, stochastic algorithms and models
  • Fuzzy models: fuzzy sets, fuzzy logic, fuzzy rules, motivations for fuzzy modeling, tools for fuzzy systems
  • Neural networks: basics, supervised and unsupervised learning, main models, deep learning, selection and evaluation,tools for neural networks
  • Stochastic models: basics, evolutionary computation, fitness function, model definition, genetic algorithms, tools for genetic algoritms. 
  • Design process  for each of the technologies
  • Applications: motivations, choices, models, case studies.

Prerequisiti

 No specific background is required.


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, which cannot be acquired from books, but only from experience.


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

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


Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
30:00
45:00
Esercitazione
20:00
30:00
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 50:00 75:00

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
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
28/01/2020