Difference between revisions of "SC:SC2012"
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* 23/03/2015 - 09:00 to 09:30: Course Introduction (by Andrea Bonarini) in "Sala Seminari" (DEIB) | * 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 - 09:30 to 12:30: Feed Forwards Neural Networks (3h by Matteo Matteucci) in "Sala Seminari" (DEIB) | ||
| − | * 23/03/2015 - 14:30 to 17: | + | * 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 - 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) | * 24/03/2015 - 14:30 to 17:30: Bayesian Networks (3h by Matteo Matteucci) in "Sala Conferenze" (DEIB) | ||
| Line 26: | Line 26: | ||
* 26/03/2015 - 09:30 to 12:30: Genetic Algorithms (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) | * 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: | + | * 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== | ==Course Material & References== | ||
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* Neural networks | * Neural networks | ||
**[[Media:sc2015_nn_handout.pdf | Neural Networks slides]] | **[[Media:sc2015_nn_handout.pdf | Neural Networks slides]] | ||
| − | **[[Media | + | **[[Media:demo_ff.m | Matlab example on feedforward neural network learning]] |
| − | + | * Bayesian networks | |
| − | + | **[[Media:sc2015_bc_handout.pdf | Bayes classifier slides]] | |
| − | + | **[[Media:sc2015_bn_handout.pdf | Bayesian networks slides]] | |
| − | + | **[[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. | |
| − | + | **[[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]. | |
| − | + | * Fuzzy logic and fuzzy systems | |
| − | + | **[[Media:FuzzySystems.tgz | Fuzzy Systems slide set]] | |
| − | + | * Genetic algorithms | |
| − | + | ** [[Media:GeneticAlgorithms.pdf | Genetic algorithms slides]] | |
| − | + | ** [[Media:FuzzyGAPhD.pdf | Hybrid Genetic algorithms and fuzzy systems slides]] | |
| − | + | * Deep Learning | |
| − | <!-- | + | ** [[Media:DeepLearning.pdf | Deep learning introduction slides]] |
| − | **[[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]] | ||
Latest revision as of 23:55, 30 March 2015
Beside its title, this is a description page for the PhD course on SC2015!!!
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.
- 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
- Introduction - Soft Computing
- Neural networks
- Bayesian networks
- Bayes classifier slides
- Bayesian networks slides
- Bayesian networks without tears by Eugene Charniak on AI Magazine 12(4), 1991.
- Chapter 8 by Christopher Bishop from its book Pattern Recognition and Machine Leanrning.
- Fuzzy logic and fuzzy systems
- Genetic algorithms
- Deep Learning
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
- Bayesian Networks without Tears by Eugene Charniak
- BOOKS
- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer Series: Information Science and Statistics, 2006. Chapter 8 (sample chapter on Bayesian Networks)
- Judea Pearl. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press, 2000.
- DEVELOPMENT TOOLS
- The netica tool by Norsys
- GeNIe&Smile SMILE is a C++ library for BN and ID, and GeNIe is a GUI for it
- Bayes Net Toolbox for Matlab
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.
