Difference between revisions of "SC:SC2012"

From Chrome
Jump to: navigation, search
(Course Evaluation)
(Bayesian Networks)
Line 97: Line 97:
 
**Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer Series: Information Science and Statistics, 2006. [http://research.microsoft.com/en-us/um/people/cmbishop/prml/Bishop-PRML-sample.pdf Chapter 8 (sample chapter on Bayesian Networks)]
 
**Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer Series: Information Science and Statistics, 2006. [http://research.microsoft.com/en-us/um/people/cmbishop/prml/Bishop-PRML-sample.pdf Chapter 8 (sample chapter on Bayesian Networks)]
 
**Judea Pearl. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press, 2000.
 
**Judea Pearl. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press, 2000.
 +
*DEVELOPMENT TOOLS
 +
**[http://www.norsys.com/netica.html The netica tool by Norsys]
 +
**[http://genie.sis.pitt.edu/ GeNIe&Smile] SMILE is a C++ library for BN and ID, and GeNIe is a GUI for it
 +
**[https://code.google.com/p/bnt/ Bayes Net Toolbox for Matlab]
  
 
====Reinforcement Learning====
 
====Reinforcement Learning====

Revision as of 21:03, 26 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

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

Course Material & References

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

Course Slides


Additional Material

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

Fuzzy Systems

Neural Networks

Genetic Algorithms

Bayesian Networks

Reinforcement Learning

Course Evaluation

A small project or report on the use of one of techniques presented during the course possibly on a topic related to your PhD topic.