Difference between revisions of "SC:Soft Computing"

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__FORCETOC__
 
__FORCETOC__
  
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The following are last minute news you should be aware of ;-)
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<!--The following are last minute news you should be aware of ;-)
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   06/10/2011: the Soft Computing course starts today!
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   06/10/2011: the Soft Computing course starts today! -->
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I am the Teaching assistant of the Soft Computing course, the official site of the course is not maintained by me and it can be found [http://home.dei.polimi.it/bonarini/Didattica/SoftComputing/index.html here]. On this page I am publishing the material of my lectures for this class but you can find those also on the official course page.
  
 
==Course Aim & Organization==
 
==Course Aim & Organization==
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* '''Neural networks''': basics, supervised and unsuprvised learning, main modelsi, selection and evaluation;
 
* '''Neural networks''': basics, supervised and unsuprvised learning, main modelsi, selection and evaluation;
 
* '''Stochastic models''': basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks;
 
* '''Stochastic models''': basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks;
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* '''Hybridization''': motivations, neuro-fuzzy systems, genetic algoritms to optimize neural networks and fuzzy systems;
 
 
* '''Applications''': motivations, choices, models, case studies.
 
* '''Applications''': motivations, choices, models, case studies.
  
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===Detailed course schedule===
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<!-- ===Detailed course schedule===
  
 
A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (they will be notified to you by email).  
 
A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (they will be notified to you by email).  
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|Date || Day || Time || Room || Teacher || Topic
 
|Date || Day || Time || Room || Teacher || Topic
 
|-
 
|-
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|06/10/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
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|06/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Introduction - Fuzzy sets
 
|-
 
|-
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|10/10/2011 || Monday || 15:15 - 17:15 || || Matteo Matteucci || Perceptron and Hebbian Learning
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|10/10/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Intro to neural networks and Perceptron model
 
|-
 
|-
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|13/10/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
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|13/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy sets
 
|-
 
|-
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|17/10/2011 || Monday || 15:15 - 17:15 || || Matteo Matteucci || Feedforward topologies and Backpropagation
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|17/10/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Hebbian learning, the xor problem, from perceptron to backpropagation
 
|-
 
|-
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|20/10/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
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|20/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy logic
 
|-
 
|-
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|24/10/2011 || Monday || 15:15 - 17:15 || || Matteo Matteucci || Overfitting limitation
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|24/10/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Feedforward topologies and Backpropagation
 
|-
 
|-
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|27/10/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
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|27/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy rules - design of fuzzy systems
 
|-
 
|-
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|03/11/2011 || Thursday || 14:15 - 16:15 || || --- || ''No lecture today''
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|03/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today''
 
|-
 
|-
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|07/11/2011 || Monday || 15:15 - 17:15 || || Matteo Matteucci || Neural Network demo/exercises
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|07/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Neural Network exercises  
 
|-
 
|-
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|10/11/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
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|10/11/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Applications
 
|-
 
|-
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|14/11/2011 || Monday || 15:15 - 17:15 || || Matteo Matteucci || Neural Network Demo/Exercises
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|14/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Overfitting limitation
 
|-
 
|-
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|17/11/2011 || Thursday || 14:15 - 16:15 || || --- || ''No lecture today''
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|17/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today''
 
|-
 
|-
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|21/11/2011 || Monday || 15:15 - 17:15 || || Matteo Matteucci || Bayesian Networks
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|21/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Bayesian Networks
 
|-
 
|-
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|24/11/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
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|24/11/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design
 
|-
 
|-
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|28/11/2011 || Monday || 15:15 - 17:15 || || Matteo Matteucci || Inference in Bayesian Networks
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|28/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Inference in Bayesian Networks
 
|-
 
|-
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|01/12/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
+
|01/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design
 
|-
 
|-
−
|05/12/2011 || Monday || 15:15 - 17:15 || || Matteo Matteucci || Bayesian Networks Demo/Exercises
+
|05/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Bayesian Networks Demo/Exercises
 
|-
 
|-
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|12/12/2011 || Monday || 15:15 - 17:15 || || Andrea Bonarini ||  
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|12/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Reinforcement Learning I
 
|-
 
|-
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|15/12/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
+
|15/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Matteo Matteucci || Bayesian Networks Demo/Exercises
 
|-
 
|-
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|19/12/2011 || Monday || 15:15 - 17:15 || || Andrea Bonarini ||  
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|19/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Reinfocement Learning – Design
 
|-
 
|-
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|22/12/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
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|22/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Reinfocement Learning – Applications
 
|-
 
|-
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|09/01/2011 || Monday || 15:15 - 17:15 || || Andrea Bonarini ||  
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|09/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Evolutionary algorithms – Genetic Algorithms
 
|-
 
|-
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|12/01/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini ||  
+
|12/01/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Genetic Algorithms – Design
 
|-
 
|-
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|16/01/2011 || Monday || 15:15 - 17:15 || || Andrea Bonarini ||  
+
|16/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Genetic Algorithms – Applications
 
|-
 
|-
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|19/01/2011 || Thursday || 14:15 - 16:15 ||  || Andrea Bonarini ||  
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|19/01/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Hybrid systems
 
|-
 
|-
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|23/01/2011 || Monday || 15:15 - 17:15 || || Andrea Bonarini ||  
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|23/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Closing remarks and exercises
 
|}
 
|}
  
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===Software===
 
===Software===
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...
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* ...
  
 
===Links to sites of other Soft Computing courses ===
 
===Links to sites of other Soft Computing courses ===
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...
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* ...
  
 
===Bibliographic resources===
 
===Bibliographic resources===
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...
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* ...
  
 
==Exam Samples and Results==
 
==Exam Samples and Results==
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* Follow this link to [[Media:Risultati_SC.pdf | the results of 14/07/2011 exam]].
 
* Follow this link to [[Media:Risultati_SC.pdf | the results of 14/07/2011 exam]].
 +
-->

Latest revision as of 00:35, 25 March 2014


I am the Teaching assistant of the Soft Computing course, the official site of the course is not maintained by me and it can be found here. On this page I am publishing the material of my lectures for this class but you can find those also on the official course page.

Course Aim & Organization

Soft Computing includes technologies (Fuzzy Systems, Neural Networks, Stochastic Algorithms and models) to model complex systems and offers a powerful modeling tool for engineers and in general people needing to model phenomena. Among the application areas, we mention: data analysis, automatic control, modeling of artificial and natural phenomena, modeling of behaviors (e.g., of users and devices), 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 introduce design techniques for systems based on these technologies.

Teachers

The course is composed by a blending of lectures and exercises by the course teacher and the teaching assistant:

Course Program

  • What is Soft Computing: fuzzy systems, neural networks, stochastic algorithms and models;
  • Fuzzy models: fuzzy sets, fuzzy logic, fuzzy rules, motivations for fuzzy modeling;
  • Neural networks: basics, supervised and unsuprvised learning, main modelsi, selection and evaluation;
  • Stochastic models: basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks;
  • Applications: motivations, choices, models, case studies.


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