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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===Course Schedule===
 
===Course Schedule===
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  Please consider this schedule as tentative ...
 
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  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.  
 
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: Fuzzy Logic (4h by Andrea Bonarini)
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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: Fuzzy Logic (4h by Andrea Bonarini)
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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: Feed Forwards Neural Networks (4h by Matteo Matteucci)
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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: Feed Forwards Neural Networks (4h by Matteo Matteucci)
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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: Genetic Algorithms (4h by Andrea Bonarini)
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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: Bayesian Networks (4h by Matteo Matteucci)
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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: Estimation of Distribution Algorithms (4h by Matteo Matteucci)
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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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* 28/09/2012: Reinforcement Learning (4h by Andrea Bonarini)
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* 26/03/2015 - 09:30 to 12:30: Genetic Algorithms (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
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* 26/03/2015 - 14:30 to 17:30: Genetic Algorithms (3h by Andrea Bonarini) in "Sala Conferenze" (DEIB)
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* 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"
  
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==Course Material & Referencies==
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==Course Material & References==
  
 
The following is some suggested material to follow the course lectures organized by topic.
 
The following is some suggested material to follow the course lectures organized by topic.
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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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-->
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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===
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 +
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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**Tutorial on Fuzzy Systems
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**[http://www.seattlerobotics.org/encoder/Mar98/fuz/flindex.html Tutorial on Fuzzy Systems]
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**Tutorial on Fuzzy Logic (by J. Jantzen)
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**[http://www.iau.dtu.dk/~jj/pubs/logic.pdf Tutorial on Fuzzy Logic (by J. Jantzen)]
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**Tutorial on Fuzzy Logic (by J. Brule)
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**[http://www.austinlinks.com/Fuzzy/tutorial.html Tutorial on Fuzzy Logic (by J. Brule)]
 
*BOOKS
 
*BOOKS
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**T. Ross, Fuzzy Logic with Engineering Applications, Third Edition, Wiley, 2010 (in alternative, only for Italian Students: A. G. Pizzaleo. Fuzzy Logic: come insegneremo alle macchine a ragionare da uomini. Castelvecchi, Roma)
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**T. Ross, Fuzzy Logic with Engineering Applications, Third Edition, Wiley, 2010  
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**A. G. Pizzaleo. Fuzzy Logic: come insegneremo alle macchine a ragionare da uomini. Castelvecchi, Roma (Only for Italian Students)
 
**B. Kosko. Il Fuzzy pensiero. Baldini e Castoldi. (In English: Fuzzy Thinking. Hyperion Press)
 
**B. Kosko. Il Fuzzy pensiero. Baldini e Castoldi. (In English: Fuzzy Thinking. Hyperion Press)
 
**A. Sangalli, The Importance of Being Fuzzy, Princeton University Press
 
**A. Sangalli, The Importance of Being Fuzzy, Princeton University Press
 
*DEVELOPMENT TOOLS
 
*DEVELOPMENT TOOLS
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**XFuzzy - a set of free tools to develop fuzzy systems
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**[http://www2.imse-cnm.csic.es/Xfuzzy/ XFuzzy - a set of free tools to develop fuzzy systems]
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**FuzzyCLIPS - an extension of the NASA-developed CLIPS, a tool to implement AI systems
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**[http://en.wikipedia.org/wiki/FuzzyCLIPS FuzzyCLIPS - an extension of the NASA-developed CLIPS, a tool to implement AI systems]
 
**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====
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*Collecton of resources about NN
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*[http://www.makhfi.com/resources.htm Collecton of resources about NN]
 
*TUTORIALS
 
*TUTORIALS
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**Tutorial by Christos Stergiou and Dimitrios Siganos
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**[http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html Tutorial by Christos Stergiou and Dimitrios Siganos]
 
*BOOKS
 
*BOOKS
 
**C. Bishop, Neural Networks and Pattern Recognition, Oxford University Press, 1995
 
**C. Bishop, Neural Networks and Pattern Recognition, Oxford University Press, 1995
 
*DEVELOPMENT TOOLS
 
*DEVELOPMENT TOOLS
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**Tools to develop NN are included in many packages like Matlab and WEKA. A list of SW is available from http://www.makhfi.com/tools.htm
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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]
  
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===Genetic Algorithms===
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====Genetic Algorithms====
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*Portal for GA
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*[http://www.geneticprogramming.com/ga/index.htm Portal for GA]
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*The Illinois Genetic Algorithms Laboratory
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*[http://www.illigal.uiuc.edu/web/ The Illinois Genetic Algorithms Laboratory]
 
*TUTORIALS
 
*TUTORIALS
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**Tutorial and demos by Marek Obitko
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**[http://www.obitko.com/tutorials/genetic-algorithms/index.php Tutorial and demos by Marek Obitko]
 
*BOOKS
 
*BOOKS
 
**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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<!--
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*Portal for RL
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====Reinforcement Learning====
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*Sutton's Lab at University of Alberta.
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*[http://www-anw.cs.umass.edu/rlr/ Portal for RL]
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*[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)
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**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 ==
  
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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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