Difference between revisions of "SC:Soft Computing"
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| − | The following are last minute news you should be aware of ;-) | + | <!--The following are last minute news you should be aware of ;-) |
| − | 06/10/2011: the Soft Computing course starts today! | + | 06/10/2011: the Soft Computing course starts today! --> |
| + | |||
| + | 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; | ||
| − | |||
* '''Applications''': motivations, choices, models, case studies. | * '''Applications''': motivations, choices, models, case studies. | ||
| − | ===Detailed course schedule=== | + | <!-- ===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 | ||
|- | |- | ||
| − | |06/10/2011 || Thursday || 14:15 - 16:15 || | + | |06/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Introduction - Fuzzy sets |
|- | |- | ||
| − | |10/10/2011 || Monday || 15:15 - 17:15 || | + | |10/10/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Intro to neural networks and Perceptron model |
|- | |- | ||
| − | |13/10/2011 || Thursday || 14:15 - 16:15 || | + | |13/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy sets |
|- | |- | ||
| − | |17/10/2011 || Monday || 15:15 - 17:15 || | + | |17/10/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Hebbian learning, the xor problem, from perceptron to backpropagation |
|- | |- | ||
| − | |20/10/2011 || Thursday || 14:15 - 16:15 || | + | |20/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy logic |
|- | |- | ||
| − | |24/10/2011 || Monday || 15:15 - 17:15 || | + | |24/10/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Feedforward topologies and Backpropagation |
|- | |- | ||
| − | |27/10/2011 || Thursday || 14:15 - 16:15 || | + | |27/10/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy rules - design of fuzzy systems |
|- | |- | ||
| − | |03/11/2011 || Thursday || 14:15 - 16:15 || | + | |03/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today'' |
|- | |- | ||
| − | |07/11/2011 || Monday || 15:15 - 17:15 || | + | |07/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Neural Network exercises |
|- | |- | ||
| − | |10/11/2011 || Thursday || 14:15 - 16:15 || | + | |10/11/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Applications |
|- | |- | ||
| − | |14/11/2011 || Monday || 15:15 - 17:15 || | + | |14/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Overfitting limitation |
|- | |- | ||
| − | |17/11/2011 || Thursday || 14:15 - 16:15 || | + | |17/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today'' |
|- | |- | ||
| − | |21/11/2011 || Monday || 15:15 - 17:15 || | + | |21/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Bayesian Networks |
|- | |- | ||
| − | |24/11/2011 || Thursday || 14:15 - 16:15 || | + | |24/11/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design |
|- | |- | ||
| − | |28/11/2011 || Monday || 15:15 - 17:15 || | + | |28/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Inference in Bayesian Networks |
|- | |- | ||
| − | |01/12/2011 || Thursday || 14:15 - 16:15 || | + | |01/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design |
|- | |- | ||
| − | |05/12/2011 || Monday || 15:15 - 17:15 || | + | |05/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Bayesian Networks Demo/Exercises |
|- | |- | ||
| − | |12/12/2011 || Monday || 15:15 - 17:15 || | + | |12/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Reinforcement Learning I |
|- | |- | ||
| − | |15/12/2011 || Thursday || 14:15 - 16:15 || | + | |15/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Matteo Matteucci || Bayesian Networks Demo/Exercises |
|- | |- | ||
| − | |19/12/2011 || Monday || 15:15 - 17:15 || | + | |19/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Reinfocement Learning – Design |
|- | |- | ||
| − | |22/12/2011 || Thursday || 14:15 - 16:15 || | + | |22/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Reinfocement Learning – Applications |
|- | |- | ||
| − | |09/01/2011 || Monday || 15:15 - 17:15 || | + | |09/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Evolutionary algorithms – Genetic Algorithms |
|- | |- | ||
| − | |12/01/2011 || Thursday || 14:15 - 16:15 || | + | |12/01/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Genetic Algorithms – Design |
|- | |- | ||
| − | |16/01/2011 || Monday || 15:15 - 17:15 || | + | |16/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Genetic Algorithms – Applications |
|- | |- | ||
| − | |19/01/2011 || Thursday || 14:15 - 16:15 || || Andrea Bonarini || | + | |19/01/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Hybrid systems |
|- | |- | ||
| − | |23/01/2011 || Monday || 15:15 - 17:15 || | + | |23/01/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Closing remarks and exercises |
|} | |} | ||
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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. | 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. | ||
| − | ==Teaching Material | + | ==Teaching Material== |
Right now, the official course website is maintained by Andrea Bonarini at [http://home.dei.polimi.it/bonarini/Didattica/SoftComputing/index.html] | 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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* [[Media:Intro.pdf | Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics. | * [[Media:Intro.pdf | Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics. | ||
| − | === | + | ===Books and Papers=== |
| − | + | ||
| + | * ... | ||
| + | |||
| + | ===Useful Links=== | ||
| + | ... | ||
| + | |||
| + | * ... | ||
| + | |||
| + | ===Software=== | ||
| + | ... | ||
| + | |||
| + | * ... | ||
| + | |||
| + | ===Links to sites of other Soft Computing courses === | ||
| + | ... | ||
| + | |||
| + | * ... | ||
| + | |||
| + | ===Bibliographic resources=== | ||
| + | ... | ||
| + | |||
| + | * ... | ||
| − | + | ==Exam Samples and Results== | |
| − | + | ||
| − | + | ||
===Past Exams and Sample Questions=== | ===Past Exams and Sample Questions=== | ||
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* ... | * ... | ||
| − | ==Exam Results== | + | ===Exam Results=== |
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: | 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: | ||
* 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:
- Andrea Bonarini: the teacher
- Matteo Matteucci: 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.
