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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|-
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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 || 14:30 - 16:30 || || 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 || 14:30 - 16:30 || || --- || '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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|21/11/2011 || || || || ||  
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|17/11/2011 || Thursday || 14:15 - 16:15 || --- || --- || ''No lecture today''
 
|-
 
|-
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|24/11/2011 || || || || ||  
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|21/11/2011 || Monday || 15:15 - 17:15 || S.1.3 || Matteo Matteucci || Bayesian Networks
 
|-
 
|-
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|28/11/2011 || || || || ||  
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|24/11/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design
 
|-
 
|-
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|//2011 || || || || ||  
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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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|//2011 || || || || ||  
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|01/12/2011 || Thursday || 14:15 - 16:15 || E.G.6 || Andrea Bonarini || Fuzzy systems – Design
 
|-
 
|-
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|//2011 || || || || ||  
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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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|//2011 || || || || ||  
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|12/12/2011 || Monday || 15:15 - 17:15 || S.1.3 || Andrea Bonarini || Reinforcement Learning I
 
|-
 
|-
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|//2011 || || || ||  ||  
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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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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.
  
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==Teaching Material (the textbook)==  
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==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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===Teacher Slides===
  
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Lectures will be based on material taken from the aforementioned slides and from the following book.
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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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* [http://http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html The Elements of Statistical Learning: Data Mining, Inference, and Prediction.] by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
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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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Some additional material that could be used to prepare the oral examination will be provided together with the past homeworks.
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===Books and Papers===
  
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===Teacher Slides===
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* ...
  
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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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===Useful Links===
 +
...
  
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* [[Media:PAMI_Intro.pdf | Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised learning and two algorithms for classification (taken from ''The Elements of Statistical Learning'' book).
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* ...
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* [[Media:PAMI_LinearClassification.pdf | Linear Classification Examples]]: slides presenting images, tables and examples about (generalized) linear methods for classification (taken from ''The Elements of Statistical Learning'' book).
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* [[Media:PAMI_KernelSmoothing.pdf | Kernel Smoothing Examples]]: slides presenting images, tables and examples about Kernel Smoothing, Kernel Density Estimation and Gaussian Mixture Models (taken from ''The Elements of Statistical Learning'' book).
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* [[Media:PAMI_DTnR.pdf | Decision Trees and Classification Rules]]: these slides have been used to present decision trees and decision rules complementing the material in Ch. 9.2 of the ''The Elements of Statistical Learning'' book.
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* [[Media:PAMI_SVM.pdf | Support Vector Machines]]: these slides have been used to present Support Vector Machines (taken from ''The Elements of Statistical Learning'' book).
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===Additional Papers===
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===Software===
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Papers used to integrate the textbook
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...
  
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* Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, [http://www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf Least Angle Regression] Annals of Statistics (with discussion) (2004) 32(2), 407-499.
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* ...
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* Burges, Christopher J. C., 1998. [http://www.svms.org/tutorials/Burges1998.pdf A tutorial on support vector machines for pattern recognition]. Data Mining and Knowledge Discovery, 2(2), 121–167.
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* ...  
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===Clustering Slides===
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===Links to sites of other Soft Computing courses ===
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These are the slides used to present clustering algorithms during lectures
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...
  
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* Lesson 1: Introduction to Clustering and K-Means ([http://davide.eynard.it/teaching/2011_PAMI/slides-lecture-e1.pdf slides], [http://davide.eynard.it/teaching/2011_PAMI/handout-lecture-e1.pdf handouts])
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* ...
  
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* Lesson 2: K-Means alternatives, Hierarchical, SOM ([http://davide.eynard.it/teaching/2011_PAMI/slides-lecture-e2.pdf slides], [http://davide.eynard.it/teaching/2011_PAMI/handout-lecture-e2.pdf handouts])
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===Bibliographic resources===
 +
...
  
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* Lesson 3: Mixture of Gaussians, DBSCAN, Jarvis-Patrick ([http://davide.eynard.it/teaching/2011_PAMI/slides-lecture-e3.pdf slides], [http://davide.eynard.it/teaching/2011_PAMI/handout-lecture-e3.pdf handouts])
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* ...
  
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* Lesson 4: Evaluation measures ([http://davide.eynard.it/teaching/2011_PAMI/slides-lecture-e4.pdf slides], [http://davide.eynard.it/teaching/2011_PAMI/handout-lecture-e4.pdf handouts])
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==Exam Samples and Results==
  
 
===Past Exams and Sample Questions===
 
===Past Exams and Sample Questions===
 
These are the text of past exams to give and idea on what to expect during the class exam:
 
These are the text of past exams to give and idea on what to expect during the class exam:
  
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* [[Media:2011_06_29.pdf |29/06/2011 Exam]]
 
 
* ...
 
* ...
  
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==Exam Results==
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===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]].
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-->
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== 2011 Homework ==
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Here you can find the homework for the year 2011 and the material you need to complete it. Please read the F.A.Q. below and for any unsolved doubt contact the teachers of the course.
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* [[Media:PAMI_homework_2011_v02.pdf | Homework 2011 v02]] a minor change in the signature of the logistic regression function
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* [[Media:PAMI_homework_2011_v01.pdf | Homework 2011 v01]] text with questions and exercises
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* [[Media:dataset.txt | Dataset]] for the clustering exercise in csv format
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'''Frequently Asked Questions'''
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* '''''How do I take the square root of a matrix?''''': check the diagonalization approach from [http://en.wikipedia.org/wiki/Square_root_of_a_matrix].
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* '''''How do I compute the chi square statistics?'''': in the slide there is a cut and paste error since e_ij=R_it*C_tj as described here [http://en.wikipedia.org/wiki/Pearson's_chi-square_test]
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* '''''When it is due? In which format?''''': The homework is due on the 29/06 and should be delivered by email. Send us (all the course teachers) the .m files in a zip archive attached to this email and a link to the pdf with the written part (not to flood our mailboxes).
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* '''''Can we do that in groups? How many people per group?''''': Yes, you can work on the homework in groups, but no more than 3 people per group are allowed. Put the names of all homework authors in the pdf and in all the .m files. If you discuss something with other people, w.r.t. the people in your group, point it out in the pdf file as well.
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* '''''Can we ask questions about the exercises or the code?''''': Yes you should! First of all, there might be unclear things in the exercise descriptions and those should be clarified as soon as possible for all (this is why the homework is versioned). But you could ask for help as well, our goal is to have you all solving all the questions and get a high grade ... but we will not do the homework on you behalf ;-)
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* '''''How the optional questions are graded?''''': They compensate for possible errors in the other questions; we suggest to work on them anyway to be sure you get the maximum grading.
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* '''''How the homework will be graded?''''': we are interested in understanding if you understood or not; thus we are not interested in the result, but we want to check how you get to the result. So please: 1) clarify all the assumptions and all the steps in your exercises 2) comment as much as possible your .m files!
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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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