Difference between revisions of "SC:SC2012"

Revision as of 08:35, 12 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:

• Andrea Bonarini
• Matteo Matteucci

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

• LESSON 1
  • Introduction - Soft Computing
  • Fuzzy Sets
  • Fuzzy Logic
  • Fuzzy Rules
  • Fuzzy Applications

see here

Additional Material

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

Fuzzy Systems

• Robert Fuller's page on Fuzzy Systems
• TUTORIALS
  • Tutorial on Fuzzy Systems
  • Tutorial on Fuzzy Logic (by J. Jantzen)
  • Tutorial on Fuzzy Logic (by J. Brule)
• BOOKS
  • T. Ross, Fuzzy Logic with Engineering Applications, Third Edition, Wiley, 2010
  • 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)
  • A. Sangalli, The Importance of Being Fuzzy, Princeton University Press
• DEVELOPMENT TOOLS
  • XFuzzy - a set of free tools to develop fuzzy systems
  • 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.

Neural Networks

• Collecton of resources about NN
• TUTORIALS
  • Tutorial by Christos Stergiou and Dimitrios Siganos
• BOOKS
  • C. Bishop, Neural Networks and Pattern Recognition, Oxford University Press, 1995
• DEVELOPMENT TOOLS
  • 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

Genetic Algorithms

• Portal for GA
• The Illinois Genetic Algorithms Laboratory
• TUTORIALS
  • Tutorial and demos by Marek Obitko
• BOOKS
  • M. Mitchell. An Introduction to Genetic Algorithms. MIT Press.

Bayesian Networks

TBC

Bayesian Networks

TBC

Reinforcement Learning

• Portal for RL
• Sutton's Lab at University of Alberta.
• BOOKS
  • R. Sutton, A. G. Barto. Reinforcement Learning: an introduction. Addison-Wesley. (http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html)

Course Evaluation

TBC