Difference between revisions of "SC:SC2012"

From Chrome
Jump to: navigation, search
(Course Schedule)
(Course Schedule)
Line 21: Line 21:
  
 
* 12/09/2012 - 09:00 to 13:00: Fuzzy Logic (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
 
* 12/09/2012 - 09:00 to 13:00: Fuzzy Logic (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
* 14/09/2012: Fuzzy Logic (4h by Andrea Bonarini)
+
* 14/09/2012 - 09:00 to 13:00: Fuzzy Logic (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
* 17/09/2012: Feed Forwards Neural Networks (4h by Matteo Matteucci)
+
* 17/09/2012 - 09:00 alle 13:00: Feed Forwards Neural Networks (4h by Matteo Matteucci) in "Sala Conferenze" (DEI)
* 19/09/2012: Feed Forwards Neural Networks (4h by Matteo Matteucci)
+
* 19/09/2012 - 09:00 alle 13:00: Feed Forwards Neural Networks (4h by Matteo Matteucci) in "Sala Conferenze" (DEI)
* 21/09/2012: Genetic Algorithms (4h by Andrea Bonarini)
+
* 21/09/2012 - 09:00 alle 13:00: Genetic Algorithms (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
* 24/09/2012: Bayesian Networks (4h by Matteo Matteucci)
+
* 24/09/2012 - 09:00 alle 13:00: Bayesian Networks (4h by Matteo Matteucci) in "Sala Conferenze" (DEI)
* 26/09/2012 (TBC): Estimation of Distribution Algorithms (4h by Matteo Matteucci)  
+
* 26/09/2012 - 09:00 alle 13:00: Estimation of Distribution Algorithms (4h by Matteo Matteucci) in "Aula Alfa" (DEI via Golgi)
* 27/09/2012: Reinforcement Learning (4h by Andrea Bonarini)
+
* 27/09/2012 - 09:00 alle 13:00: Reinforcement Learning (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
  
 
==Course Material & Referencies==
 
==Course Material & Referencies==

Revision as of 23:46, 5 September 2012

This is a description page for the PhD course on SC2012.


Course Aim & Organization

Soft Computing includes technologies (Fuzzy Systems, Neural Networks, Stochastic Algorithms , Bayesian Networks, ...) to model complex systems and offer a powerful tool both for research and companies in different, rapidly growing application areas, such as, for instance: data analysis, automatic control, modeling of artificial and natural phoenomena, modeling of behaviors (e.g., of users), 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 describe how to design systems based on these technologies. No specific background is required. In past editions the course has been followed by people with many different backgrounds among which: all engineering specialties, biology, vulcanology, architecture.

Teachers

The course will be held by:

Course Schedule

 Please consider this schedule as tentative ... 
 The course will start in September 2012, details about lecture time will be available soon

In the following you find the detailed schedule for the course and the rooms booked for it. In brackets you find also the lecturer for each specific topic.

  • 12/09/2012 - 09:00 to 13:00: Fuzzy Logic (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
  • 14/09/2012 - 09:00 to 13:00: Fuzzy Logic (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
  • 17/09/2012 - 09:00 alle 13:00: Feed Forwards Neural Networks (4h by Matteo Matteucci) in "Sala Conferenze" (DEI)
  • 19/09/2012 - 09:00 alle 13:00: Feed Forwards Neural Networks (4h by Matteo Matteucci) in "Sala Conferenze" (DEI)
  • 21/09/2012 - 09:00 alle 13:00: Genetic Algorithms (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
  • 24/09/2012 - 09:00 alle 13:00: Bayesian Networks (4h by Matteo Matteucci) in "Sala Conferenze" (DEI)
  • 26/09/2012 - 09:00 alle 13:00: Estimation of Distribution Algorithms (4h by Matteo Matteucci) in "Aula Alfa" (DEI via Golgi)
  • 27/09/2012 - 09:00 alle 13:00: Reinforcement Learning (4h by Andrea Bonarini) in "Sala Seminari" (DEI)

Course Material & Referencies

The following is some suggested material to follow the course lectures organized by topic.

Course Slides

TBC

Fuzzy Systems

Neural Networks

Genetic Algorithms

Bayesian Networks

TBC

Bayesian Networks

TBC

Reinforcement Learning

Course Evaluation

TBC