Difference between revisions of "SC:SC2012"

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*LESSON 1
 
*LESSON 1
 
**[[Media:IntroSoftComputingPhD.pdf | Introduction - Soft Computing]]
 
**[[Media:IntroSoftComputingPhD.pdf | Introduction - Soft Computing]]
 +
**[[Media:sc2012_nn_handout.pdf | Neural Networks]]
 +
*LESSON 2
 +
**[[Media:sc2012_bn_handout.pdf| Bayesian Networks]]
 +
*LESSON 3
 
**[[Media:IntroFuzzySetsPhD.pdf | Fuzzy Sets]]
 
**[[Media:IntroFuzzySetsPhD.pdf | Fuzzy Sets]]
 
**[[Media:IntroFuzzyLogicPhD.pdf |Fuzzy Logic]]
 
**[[Media:IntroFuzzyLogicPhD.pdf |Fuzzy Logic]]
 
**[[Media:FuzzyRulesPhD.pdf |Fuzzy Rules]]
 
**[[Media:FuzzyRulesPhD.pdf |Fuzzy Rules]]
 
**[[Media:FuzzyApplicationsPhD.pdf |Fuzzy Applications]]
 
**[[Media:FuzzyApplicationsPhD.pdf |Fuzzy Applications]]
−
*LESSON 2
 
 
**[[Media:IntroFuzzyMathPhD.pdf | Fuzzy Arithmetics]]
 
**[[Media:IntroFuzzyMathPhD.pdf | Fuzzy Arithmetics]]
 
**[[Media:FuzzyDesignPhD.pdf | Fuzzy Design]]
 
**[[Media:FuzzyDesignPhD.pdf | Fuzzy Design]]
 
**[[Media:CaseStudyFuzzy.pdf | Case study]]
 
**[[Media:CaseStudyFuzzy.pdf | Case study]]
−
*LESSON 3 + 4
+
*LESSON 4
−
**[[Media:sc2012_nn_handout.pdf | Neural Networks]]
+
<--
−
*LESSON 5
+
 
**[[Media:handout-lecture-GA.pdf | Genetic Algorithms]]
 
**[[Media:handout-lecture-GA.pdf | Genetic Algorithms]]
 
**[[Media:LCSPhD.pdf| Learning Classifier Systems]]
 
**[[Media:LCSPhD.pdf| Learning Classifier Systems]]
 
**[[Media:FuzzyGAPhD.pdf| Fuzzy Genetic Algorithms]]
 
**[[Media:FuzzyGAPhD.pdf| Fuzzy Genetic Algorithms]]
−
*LESSON 6
 
−
**[[Media:sc2012_bn_handout.pdf| Bayesian Networks]]
 
−
*LESSON 7
 
 
**[[Media:SP_EDAs_EVO.pdf| Estimation of Distribution Algorithms]] by [http://www-users.cs.york.ac.uk/smp/ Simon Poulding @ University of York]
 
**[[Media:SP_EDAs_EVO.pdf| Estimation of Distribution Algorithms]] by [http://www-users.cs.york.ac.uk/smp/ Simon Poulding @ University of York]
−
*LESSON 8
+
*LESSON 5
 +
!-->
 +
<--
 
**[[Media:ReinforcementLearningPhD.pdf| Reinforcement Learning 1]]
 
**[[Media:ReinforcementLearningPhD.pdf| Reinforcement Learning 1]]
 
**[[Media:ReinforcementLearningIIPhD.pdf| Reinforcement Learning 2]]
 
**[[Media:ReinforcementLearningIIPhD.pdf| Reinforcement Learning 2]]
 
**[[Media:ReinforcementLearningExamplesAndDesignPhD.pdf| Reinforcement Learning Design and Examples]]
 
**[[Media:ReinforcementLearningExamplesAndDesignPhD.pdf| Reinforcement Learning Design and Examples]]
 
**[[Media:ReinforcementLearningApplicationsPhD.pdf| Reinforcement Learning Applications]]
 
**[[Media:ReinforcementLearningApplicationsPhD.pdf| Reinforcement Learning Applications]]
 +
!-->
 
<!-- see [http://home.dei.polimi.it/bonarini/Didattica/SC2012PhD/materiale.html here]-->
 
<!-- see [http://home.dei.polimi.it/bonarini/Didattica/SC2012PhD/materiale.html here]-->
  

Revision as of 17:23, 14 January 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 17:30: 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: TBD (3h by Andrea Bonarini)
  • 27/03/2015 - 14:30 to 17:30: TBD (3h by Matteo Matteucci)

Course Material & References

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

Course Slides

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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.

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