Difference between revisions of "SC:SC2012"
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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. | ||
| − | ===Neural Networks=== | + | ====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] | ||
| − | ===Genetic Algorithms=== | + | ====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. | ||
| − | ===Bayesian Networks=== | + | ====Bayesian Networks==== |
TBC | TBC | ||
| − | = | + | ====Reinforcement Learning==== |
| − | + | ||
| − | + | ||
| − | + | ||
| − | ===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.] | ||
Revision as of 11:44, 12 September 2012
This is a description page for the PhD course on SC2012.
Contents
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
- LESSON 1
- LESSON 2
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
- 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
- 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
- BOOKS
- M. Mitchell. An Introduction to Genetic Algorithms. MIT Press.
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
