Difference between revisions of "SC:SC2012"

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This is a description page for the PhD course on ''SC2012''.
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Beside its title, this is a description page for the PhD course on ''SC2015''!!!
  
 
__FORCETOC__
 
__FORCETOC__
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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.  
 
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.  
  
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* 12/09/2012 - 09:00 to 13:00: Fuzzy Logic (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
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* 23/03/2015 - 09:00 to 09:30: Course Introduction (by Andrea Bonarini) in "Sala Seminari" (DEIB)
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* 14/09/2012 - 09:00 to 13:00: Fuzzy Logic (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
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* 23/03/2015 - 09:30 to 12:30: Feed Forwards Neural Networks (3h by Matteo Matteucci) in "Sala Seminari" (DEIB)
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* 17/09/2012 - 09:00 to 13:00: Feed Forwards Neural Networks (4h by Matteo Matteucci) in "Sala Conferenze" (DEI)
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* 23/03/2015 - 14:30 to 17:00: Feed Forwards Neural Networks (3h by Matteo Matteucci) in "Sala Seminari" (DEIB)
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* 19/09/2012 - 09:00 to 13:00: Feed Forwards Neural Networks (4h by Matteo Matteucci) in "Sala Conferenze" (DEI)
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* 24/03/2015 - 09:30 to 12:30: Bayesian Networks (3h by Matteo Matteucci) in "Sala Conferenze" (DEIB)
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* 21/09/2012 - 09:00 to 13:00: Genetic Algorithms (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
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* 24/03/2015 - 14:30 to 17:30: Bayesian Networks (3h by Matteo Matteucci) in "Sala Conferenze" (DEIB)
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* 24/09/2012 - 09:00 to 13:00: Bayesian Networks (4h by Matteo Matteucci) in "Sala Conferenze" (DEI)
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* 25/03/2015 - 09:30 to 12:30: Fuzzy Logic (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
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* 26/09/2012 - 09:00 to 13:00: Estimation of Distribution Algorithms (4h by Matteo Matteucci) in "Aula Alfa" (DEI via Golgi)
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* 25/03/2015 - 14:30 to 17:30: Fuzzy Logic (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
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* 27/09/2012 - 09:00 to 13:00: Reinforcement Learning (4h by Andrea Bonarini) in "Sala Seminari" (DEI)
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* 26/03/2015 - 09:30 to 12:30: Genetic Algorithms (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
 +
* 26/03/2015 - 14:30 to 17:30: Genetic Algorithms (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
 +
* 27/03/2015 - 09:30 to 12:30: Fuzzy system design (3h by Andrea Bonarini) in "Sala Conferenze"
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* 30/03/2015 - 15:00 to 18:00: TBD (3h by Matteo Matteucci) in "Sala Conferenze"
  
 
==Course Material & References==
 
==Course Material & References==
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===Course Slides===
 
===Course Slides===
  
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TBC
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*[[Media:IntroSoftComputingPhD.pdf | Introduction - Soft Computing]]
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* Neural networks
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**[[Media:sc2015_nn_handout.pdf | Neural Networks slides]]
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**[[Media:demo_ff.m | Matlab example on feedforward neural network learning]]
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* Bayesian networks
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**[[Media:sc2015_bc_handout.pdf | Bayes classifier slides]]
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**[[Media:sc2015_bn_handout.pdf | Bayesian networks slides]]
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**[[Media:BayesNetNoTears.pdf | Bayesian networks without tears]] by Eugene Charniak on [http://www.aaai.org/ojs/index.php/aimagazine/article/view/918 AI Magazine 12(4)], 1991.
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**[[Media:Chapter8_Bishop.pdf | Chapter 8]] by Christopher Bishop from its book [http://research.microsoft.com/en-us/um/people/cmbishop/prml/ Pattern Recognition and Machine Leanrning].
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* Fuzzy logic and fuzzy systems
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**[[Media:FuzzySystems.tgz | Fuzzy Systems slide set]]
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* Genetic algorithms
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** [[Media:GeneticAlgorithms.pdf | Genetic algorithms slides]]
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** [[Media:FuzzyGAPhD.pdf | Hybrid Genetic algorithms and fuzzy systems slides]]
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* Deep Learning
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** [[Media:DeepLearning.pdf | Deep learning introduction slides]]
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<!--**[[Media:handout-lecture-GA.pdf | Genetic Algorithms]]
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**[[Media:LCSPhD.pdf| Learning Classifier Systems]]
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**[[Media:FuzzyGAPhD.pdf| Fuzzy Genetic Algorithms]]
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**[[Media:SP_EDAs_EVO.pdf| Estimation of Distribution Algorithms]] by [http://www-users.cs.york.ac.uk/smp/ Simon Poulding @ University of York]
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**[[Media:ReinforcementLearningPhD.pdf| Reinforcement Learning 1]]
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**[[Media:ReinforcementLearningIIPhD.pdf| Reinforcement Learning 2]]
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**[[Media:ReinforcementLearningExamplesAndDesignPhD.pdf| Reinforcement Learning Design and Examples]]
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**[[Media:ReinforcementLearningApplicationsPhD.pdf| Reinforcement Learning Applications]]
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-->
 +
<!-- see [http://home.dei.polimi.it/bonarini/Didattica/SC2012PhD/materiale.html here]-->
  
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===Fuzzy Systems===
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===Additional Material===
 +
 
 +
The following is some suggested material to follow the course lectures organized by topic.
 +
 
 +
====Fuzzy Systems====
 
*[http://www.abo.fi/~rfuller/fuzs.html Robert Fuller's page on Fuzzy Systems]
 
*[http://www.abo.fi/~rfuller/fuzs.html Robert Fuller's page on Fuzzy Systems]
 
*TUTORIALS
 
*TUTORIALS
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**Tools to develop Fuzzy systems are included in Matlab and many other modeling and applicative tools.
 
**Tools to develop Fuzzy systems are included in Matlab and many other modeling and applicative tools.
  
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===Neural Networks===
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====Neural Networks====
 
*[http://www.makhfi.com/resources.htm Collecton of resources about NN]
 
*[http://www.makhfi.com/resources.htm Collecton of resources about NN]
 
*TUTORIALS
 
*TUTORIALS
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**Tools to develop NN are included in many packages like Matlab and [http://www.makhfi.com/tools.htm%20target= WEKA]. A list of SW is available from [http://www.makhfi.com/tools.htm http://www.makhfi.com/tools.htm]
 
**Tools to develop NN are included in many packages like Matlab and [http://www.makhfi.com/tools.htm%20target= WEKA]. A list of SW is available from [http://www.makhfi.com/tools.htm http://www.makhfi.com/tools.htm]
  
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===Genetic Algorithms===
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====Genetic Algorithms====
 
*[http://www.geneticprogramming.com/ga/index.htm Portal for GA]
 
*[http://www.geneticprogramming.com/ga/index.htm Portal for GA]
 
*[http://www.illigal.uiuc.edu/web/ The Illinois Genetic Algorithms Laboratory]
 
*[http://www.illigal.uiuc.edu/web/ The Illinois Genetic Algorithms Laboratory]
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**M. Mitchell. An Introduction to Genetic Algorithms. MIT Press.
 
**M. Mitchell. An Introduction to Genetic Algorithms. MIT Press.
  
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===Bayesian Networks===
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====Bayesian Networks====
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*[http://www.kddresearch.org/Resources/Papers/Intro/notears.pdf Bayesian Networks without Tears by Eugene Charniak]
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TBC
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*BOOKS
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**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)]
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===Bayesian Networks===
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**Judea Pearl. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press, 2000.
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*DEVELOPMENT TOOLS
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TBC
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**[http://www.norsys.com/netica.html The netica tool by Norsys]
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**[http://genie.sis.pitt.edu/ GeNIe&Smile] SMILE is a C++ library for BN and ID, and GeNIe is a GUI for it
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**[https://code.google.com/p/bnt/ Bayes Net Toolbox for Matlab]
  
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===Reinforcement Learning===
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<!--
 +
====Reinforcement Learning====
 
*[http://www-anw.cs.umass.edu/rlr/ Portal for RL]
 
*[http://www-anw.cs.umass.edu/rlr/ Portal for RL]
 
*[http://rlai.cs.ualberta.ca/RLAI/rlai.html Sutton's Lab at University of Alberta.]
 
*[http://rlai.cs.ualberta.ca/RLAI/rlai.html Sutton's Lab at University of Alberta.]
 
*BOOKS
 
*BOOKS
 
**R. Sutton, A. G. Barto. Reinforcement Learning: an introduction. Addison-Wesley. ([http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html])
 
**R. Sutton, A. G. Barto. Reinforcement Learning: an introduction. Addison-Wesley. ([http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html])
 +
-->
  
 
== Course Evaluation ==
 
== Course Evaluation ==
  
−
TBC
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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.

Latest revision as of 23:55, 30 March 2015

Beside its title, this is a description page for the PhD course on SC2015!!!


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.

  • 23/03/2015 - 09:00 to 09:30: Course Introduction (by Andrea Bonarini) in "Sala Seminari" (DEIB)
  • 23/03/2015 - 09:30 to 12:30: Feed Forwards Neural Networks (3h by Matteo Matteucci) in "Sala Seminari" (DEIB)
  • 23/03/2015 - 14:30 to 17:00: Feed Forwards Neural Networks (3h by Matteo Matteucci) in "Sala Seminari" (DEIB)
  • 24/03/2015 - 09:30 to 12:30: Bayesian Networks (3h by Matteo Matteucci) in "Sala Conferenze" (DEIB)
  • 24/03/2015 - 14:30 to 17:30: Bayesian Networks (3h by Matteo Matteucci) in "Sala Conferenze" (DEIB)
  • 25/03/2015 - 09:30 to 12:30: Fuzzy Logic (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
  • 25/03/2015 - 14:30 to 17:30: Fuzzy Logic (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
  • 26/03/2015 - 09:30 to 12:30: Genetic Algorithms (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
  • 26/03/2015 - 14:30 to 17:30: Genetic Algorithms (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
  • 27/03/2015 - 09:30 to 12:30: Fuzzy system design (3h by Andrea Bonarini) in "Sala Conferenze"
  • 30/03/2015 - 15:00 to 18:00: TBD (3h by Matteo Matteucci) in "Sala Conferenze"

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


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.

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