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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   27/07/2011: published the results from the 14/07/2011 exam
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   06/10/2011: the Soft Computing course starts today! -->
  
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==Course Website and Material==
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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.
  
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The official course website is maintained by Andrea Bonarini at [http://home.dei.polimi.it/bonarini/Didattica/SoftComputing/index.html]
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==Course Aim & Organization==
  
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==Exam Results==
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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.
  
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* Follow this link to [[Media:Risultati_SC.pdf | the results of 14/07/2011 exam]].  
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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.  
  
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''Note'' that two rows are not complete, this is because in one case Part II is missing (need to double check if it was written in the same sheet of Part I) and in the other case there is no author name on the sheet of Part II (need to figure out the author double checking on Part I) ... these issues will be sorted out tomorrow.
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===Teachers===
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The course is composed by a blending of lectures and exercises by the course teacher and the teaching assistant:
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* [http://www.dei.polimi.it/people/bonarini Andrea Bonarini]: the teacher
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* [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the teaching assistant
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===Course Program===
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* '''What is Soft Computing''': fuzzy systems, neural networks, stochastic algorithms and models;
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* '''Fuzzy models''': fuzzy sets, fuzzy logic, fuzzy rules, motivations for fuzzy modeling;
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* '''Neural networks''': basics, supervised and unsuprvised learning, main modelsi, selection and evaluation;
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* '''Stochastic models''': basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks;
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* '''Applications''': motivations, choices, models, case studies.
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<!-- ===Detailed course schedule===
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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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{| border="1" align="center" style="text-align:center;"
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|-
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|Date || Day || Time || Room || Teacher || Topic
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|-
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|06/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Introduction - Fuzzy sets
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|-
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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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|-
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|13/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy sets
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|-
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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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|-
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|20/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy logic
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|-
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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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|-
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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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|-
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|03/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today''
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|-
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|07/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Neural Network exercises
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|-
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|10/11/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Applications
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|-
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|14/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Overfitting limitation
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|-
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|17/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today''
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|-
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|21/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Bayesian Networks
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|-
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|24/11/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design
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|-
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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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|-
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|01/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design
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|-
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|05/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Bayesian Networks Demo/Exercises
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|-
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|12/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Reinforcement Learning I
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|-
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|15/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Matteo Matteucci || Bayesian Networks Demo/Exercises
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|-
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|19/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Reinfocement Learning – Design
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|-
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|22/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Reinfocement Learning – Applications
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|-
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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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|-
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|12/01/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Genetic Algorithms – Design
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|-
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|16/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Genetic Algorithms – Applications
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|-
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|19/01/2011 || Thursday || 14:15 - 16:15 || E.G.6  || Andrea Bonarini || Hybrid systems
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|-
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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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|}
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===Course Evaluation===
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The exam is a test done in regular sessions, starting from the end of the lessons. The test is partitioned in two parts, whose evaluation is averaged. For each of them 32 points are available and a minimum of 15 is needed. The average vote must be greater or equal to 18 to pass the exam. Some example from past years are available below. From year 2011 the format of the exam will change a little bit, but the type of questions will analogous. An example of the format for this year will be published later.
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This course can be taken as a stand alone course or as a course integrated with Artificial Intelligence. In both cases, the course will be offered at the same time to students taking one or the other format. The exam will be also the same, but, in the case of integrated course, it will have to be passed together with the exam of Artificial Intelligence, as a unique exam, the same day. The same rules apply for the exam of the integrated course, and the marks obtained in SC and AI will be averaged. The difference between the two solutions is that the integrated course can be selected as a unique course in the study plan.
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==Teaching Material==
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Right now, the official course website is maintained by Andrea Bonarini at [http://home.dei.polimi.it/bonarini/Didattica/SoftComputing/index.html]
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===Teacher Slides===
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In the following you can find the lecture slides used by the teacher and the teaching assistants during classes:
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* [[Media:Intro.pdf | Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics.
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===Books and Papers===
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* ...
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===Useful Links===
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...
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* ...
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===Software===
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...
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* ...
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===Links to sites of other Soft Computing courses ===
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...
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* ...
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===Bibliographic resources===
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...
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* ...
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==Exam Samples and Results==
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===Past Exams and Sample Questions===
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These are the text of past exams to give and idea on what to expect during the class exam:
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* ...
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===Exam Results===
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From time to time, you can find here results for the Soft Computing exams, please refer to the official course website for up to date news:
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 +
* 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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