Difference between revisions of "Methodologies for Intelligent Systems"

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__FORCETOC__
 
__FORCETOC__
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The following are last minute news you should be aware of ;-)
 +
07/08/2010: Summer grades are out details are [[Media:Grades_Summer_2010.pdf |here!]]
 +
23/07/2010: the exam of 26/07/2010 will be in room 1.4 starting at 9:00 am
 +
29/06/2010: homework EXTENSION, new deadline 10/07/2010
 +
16/06/2010: the homework is out! Turn it in by 05/07/2010 !!!
 +
09/03/2010: the course starts today!
  
 
==Course Aim & Organization==
 
==Course Aim & Organization==
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* [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the course teacher
 
* [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the course teacher
 
* [http://www.dei.polimi.it/people/eynard Davide Eynard]: the teaching assistant on clustering
 
* [http://www.dei.polimi.it/people/eynard Davide Eynard]: the teaching assistant on clustering
−
* [http://www.dei.polimi.it/people/blatt Rossella Blatt]: the teaching assistant on feature selection/projection
+
* [http://www.dei.polimi.it/people/tognetti Simone Tognetti]: the teaching assistant on feature selection/projection
  
 
===Course Program and Schedule===
 
===Course Program and Schedule===
Line 17: Line 24:
 
These techniques are presented from a theoretical (i.e., statistics and information theory) and practical perspective through the descriptions of algorithms, their implementation, and applications.The course is composed by a set of selfcontained lectures on specific techniques such as decision trees, decision rules, Bayesian networks, clustering, etc. Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is:
 
These techniques are presented from a theoretical (i.e., statistics and information theory) and practical perspective through the descriptions of algorithms, their implementation, and applications.The course is composed by a set of selfcontained lectures on specific techniques such as decision trees, decision rules, Bayesian networks, clustering, etc. Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is:
  
−
* 'Machine Learning and Pattern Classification': in this part of the course the general concepts of Machine Learning and Patter Recognition are introduced with a brief review of statistics and information theory;  
+
* '''''Machine Learning and Pattern Classification''''': in this part of the course the general concepts of Machine Learning and Patter Recognition are introduced with a brief review of statistics and information theory;  
−
* Unsupervised Learning Techniques: the most common approaches to unsupervised learning are described mostly focusing on clustering techniques, rule induction, Bayesian networks and density estimators using mixure models;  
+
* '''''Unsupervised Learning Techniques''''': the most common approaches to unsupervised learning are described mostly focusing on clustering techniques, rule induction, Bayesian networks and density estimators using mixure models;  
−
* Supervised Learning Techniques:  in this part of the course the most common techniques for Supervised Learning are described: decision trees, decision rules, Bayesian classifiers, hidden markov models, lazy learners, etc.
+
* '''''Supervised Learning Techniques''''':  in this part of the course the most common techniques for Supervised Learning are described: decision trees, decision rules, Bayesian classifiers, hidden markov models, lazy learners, etc.
−
* Feature Selection and Reduction: techniques for data rediction and feature selection will be presented with theory and applications  
+
* '''''Feature Selection and Reduction''''': techniques for data rediction and feature selection will be presented with theory and applications  
−
* Model Validation and Selection: model validation and selection are orthogonal issues to previous technique; during the course the fundamentals are described and discussed (e.g., AIC, BIC, cross-validation, etc. ).
+
* '''''Model Validation and Selection''''': model validation and selection are orthogonal issues to previous technique; during the course the fundamentals are described and discussed (e.g., AIC, BIC, cross-validation, etc. ).
  
 
A detailed schedule of the course can be found here; topics are just indicative while days and teachers are corret "up to some last minute change".
 
A detailed schedule of the course can be found here; topics are just indicative while days and teachers are corret "up to some last minute change".
  
−
==Teaching Material==
+
===Course Evaluation===
  
−
Lecture Slides on Machine Learning and Pattern Recognition
+
The course evaluation is composed by two parts:
  
−
* Lecture 1: Introduction to Machine Learning
+
* A homework with exercises covering the whole program that counts for 30% of the course grade
−
* Lecture 2: Probability for Dataminers
+
* A oral examination covering the whole progran that count for 70% of the course grade
−
* Lecture 3: Decision Trees
+
−
* Lecture 4: Decision Rules
+
−
* Lecture 5: Bayesian Classifiers
+
−
* Lecture 6: Bayesian Networks
+
−
* Lecture 7: Markov Chains and Hidden Markov Models
+
  
−
Lecture Slides on Clustering
+
The homework is just one per year, it will be published at the end of the course and you will have 15 days to turn it in. It is not mandatory, however if you do not turn it in you loose 30% of the course grade. There is the option of substitute the homework with a practical project, but this has to be discussed and agreed with the course professor.
  
−
* Clustering Lecture 1: Introduction (03/04/2008)
+
==Teaching Material==
−
* Clustering Lecture 2: K-Means and Hierarchical (03/04/2008)
+
−
* Clustering Lecture 3: Fuzzy, Gaussians, and SOM (03/04/2008)
+
−
* Clustering Lecture 4: Vector Spacec Model and PDDP
+
−
* Clustering Lecture 5: DBSCAN and Jarvis Patrick
+
−
* Clustering Lecture 6: Evaluation measures
+
  
−
Lecture Slides on Dimensionality Reduction and Feature Selection
+
In the following you can find the lecture slides used by the teacher and the teaching assistants during classes. Some additional material that could be used to prepare the oral examination is provided as well together with the homework.
  
−
* Dimensionality Reduction Lecture 1: Dimensionality reduction Intro
+
===Machine Learning and Pattern Recognition===
−
* Dimensionality Reduction Lecture 2: Feature extraction, PCA and LDA
+
−
* Dimensionality Reduction Lecture 3: Feature selection
+
−
* Genetic Algorithms: a rather comprehensive tutorial
+
−
* Algorithm Evaluation: from cross-validation to confidence intervals
+
  
−
A few additional lecture notes
+
* [[Media:Mis-handout-lecture-1.pdf | Lecture 1]]: Introduction to Machine Learning
 +
* [[Media:Mis-handout-lecture-2.pdf | Lecture 2]]: Probability for Dataminers
 +
* [[Media:Mis-handout-lecture-3.pdf | Lecture 3]]: Decision Trees
 +
* [[Media:Mis-handout-lecture-4.pdf | Lecture 4]]: Decision Rules
 +
* [[Media:Mis-handout-lecture-5.pdf | Lecture 5]]: Bayesian Classifiers
 +
* [[Media:Mis-handout-lecture-6.pdf | Lecture 6]]: Bayesian Networks
 +
* [[Media:Mis-handout-lecture-7.pdf | Lecture 7]]: Markov Chains and Hidden Markov Models
  
−
* Fundamental Problems for HMM: a document to introduce Hidden Markov Models and the three fundamental questions about them.
+
===Clustering===
−
* An exercise on modeling and reasoning with Bayesian Networks.
+
 
 +
* [http://davide.eynard.it/teaching/2010_msi/handout-lecture-e1.pdf Clustering Lecture 1]: Introduction
 +
* [http://davide.eynard.it/teaching/2010_msi/handout-lecture-e2.pdf Clustering Lecture 2]: K-Means and Hierarchical
 +
* [http://davide.eynard.it/teaching/2010_msi/handout-lecture-e3.pdf Clustering Lecture 3]: Fuzzy, Gaussians, and SOM
 +
* [http://davide.eynard.it/teaching/2010_msi/handout-lecture-e4.pdf Clustering Lecture 4]: Vector Spacec Model and PDDP
 +
* [http://davide.eynard.it/teaching/2010_msi/handout-lecture-e5.pdf Clustering Lecture 5]: DBSCAN and Jarvis Patrick
 +
* [http://davide.eynard.it/teaching/2010_msi/handout-lecture-e6.pdf Clustering Lecture 6]: Evaluation measures
 +
 
 +
===Dimensionality Reduction and Feature Selection===
 +
 
 +
* [[Media:01-Introduction.pdf | Feature Lecture 1]]: Dimensionality reduction Intro and Feature extraction
 +
** [[Media:Lezione01.txt | Matlab example for the first lecture]] (rename it as lezione.m)
 +
* [[Media:02-FeatureProjection.pdf| Feature Lecture 2]]: Feature projection, PCA and LDA
 +
** [[Media:LDA.txt | Matlab LDA example]] (rename it as LDA.m)
 +
* [[Media:03-FeatureSelection.pdf | Feature Lecture 3]]: Feature selection methods
 +
* [[Media:04-GeneticAlgorithms.pdf | Genetic Algorithm]]: Lecture about genetic algorithms
 +
* [[Media:05-Crossvalidation.pdf | Cross Validation]]: Lecture about cross validation and model evaluation techniques
 +
** [[Media:Lezione04a.txt | Matlab example for the fourth lecture]] (rename it as lezione04a.m)
 +
** [[Media:Lezione04.txt | Matlab example for the fourth lecture]] (rename it as lezione04.m)
 +
** [[Media:Lez_weka.txt | Matlab example for the fourth lecture]] (rename it as lez_weka.m)
 +
** [[Media:Matlab2weka.txt | Matlab example for the fourth lecture]] (rename it as matlab2weka.m)
 +
** [[Media:Plot_distribution2.txt | Matlab example for the fourth lecture]] (rename it as plot_distribution2.m)
 +
** [[Media:Test_weka.txt | Matlab example for the fourth lecture]] (rename it as test_weka.m)
 +
** [[Media:Train_weka_classif_affective.txt | Matlab example for the fourth lecture]] (rename it as train_weka_classif_affective.m)
  
 
===Homeworks===
 
===Homeworks===
  
 
The homework, although not mandatory, counts for the 30% of the course grade (i.e., if you do not turn it in you loose 30% of the final grade).  
 
The homework, although not mandatory, counts for the 30% of the course grade (i.e., if you do not turn it in you loose 30% of the final grade).  
−
You have 15 days to turn it in to the teacher. '''This year the homework is due by the 3rd of July!'''
+
You have 15 days to turn it in to the teacher. '''This year the homework is due by the 5th of July!''' Please turn in a digital (or digitalized) copy of your homework.
  
−
* Homework for the academic year 2008/2009
+
* [[Media:Homework_2009-2010.pdf | Homework for the academic year 2009/2010]]
  
 
Past years course homework; you can use them to make some practice and prepare this year homework ;-)
 
Past years course homework; you can use them to make some practice and prepare this year homework ;-)
  
−
* Homework for the academic year 2007/2008
+
* [http://home.dei.polimi.it/matteucc/lectures/MIS/Homework_2008-2009.pdf Homework for the academic year 2008/2009]
−
* Homework for the academic year 2006/2007
+
* [http://home.dei.polimi.it/matteucc/lectures/MIS/Homework_2007-2008_1.pdf Homework for the academic year 2007/2008 Part 1] and [http://home.dei.polimi.it/matteucc/lectures/MIS/Homework_2007-2008_2.pdf Part 2]
−
* Homework for the academic year 2005/2006
+
* [http://home.dei.polimi.it/matteucc/lectures/MIS/Homework_2006-2007.pdf Homework for the academic year 2006/2007]
 +
* [http://home.dei.polimi.it/matteucc/lectures/MIS/Homework_2005-2006.pdf Homework for the academic year 2005/2006]
 +
 
 +
===Additional Lecture Notes and Bibliography===
 +
 
 +
* [http://people.cs.ubc.ca/~murphyk/Bayes/Charniak_91.pdf Bayesian Networks without tears]: a useful introduction to Bayesian Network.
 +
* [http://home.dei.polimi.it/matteucc/lectures/MIS/FundamentalIssuesHMM.pdf Fundamental Problems for HMM]: a document to introduce Hidden Markov Models and the three fundamental questions about them.
 +
* [http://home.dei.polimi.it/matteucc/lectures/MIS/BayesianSolution.pdf An exercise on modeling and reasoning with Bayesian Networks].
 +
* The Pearl's message passing algorithm deserves some extra thoughts
 +
** [http://en.wikipedia.org/wiki/Belief_propagation The wikipedia article and related references]
 +
** [http://www.google.com/url?sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http%3A%2F%2Fwww.cs.pitt.edu%2F~tomas%2Fcs3750%2Fpearl.ppt&ei=gb0WTIu7FISZ_QaBjYCACA&usg=AFQjCNGVGKkp17sCn2M-Gg3_FuYyigLmeA Powerpoint Slides by Tomas Singliar]
 +
** [http://whatdafact.com/data_kittipat/Note_on_Pearls_message_passing.pdf Some handwritten notes by Kittipat Kampa]

Latest revision as of 18:07, 7 August 2010


The following are last minute news you should be aware of ;-) 

07/08/2010: Summer grades are out details are here!
23/07/2010: the exam of 26/07/2010 will be in room 1.4 starting at 9:00 am
29/06/2010: homework EXTENSION, new deadline 10/07/2010
16/06/2010: the homework is out! Turn it in by 05/07/2010 !!!
09/03/2010: the course starts today!

Course Aim & Organization

The objective of this course is to give an advanced presentation of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling.

Teachers

The course is composed by a blending of lectures and exercises by the course teacher and some teaching assistants.

Course Program and Schedule

These techniques are presented from a theoretical (i.e., statistics and information theory) and practical perspective through the descriptions of algorithms, their implementation, and applications.The course is composed by a set of selfcontained lectures on specific techniques such as decision trees, decision rules, Bayesian networks, clustering, etc. Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is:

  • Machine Learning and Pattern Classification: in this part of the course the general concepts of Machine Learning and Patter Recognition are introduced with a brief review of statistics and information theory;
  • Unsupervised Learning Techniques: the most common approaches to unsupervised learning are described mostly focusing on clustering techniques, rule induction, Bayesian networks and density estimators using mixure models;
  • Supervised Learning Techniques: in this part of the course the most common techniques for Supervised Learning are described: decision trees, decision rules, Bayesian classifiers, hidden markov models, lazy learners, etc.
  • Feature Selection and Reduction: techniques for data rediction and feature selection will be presented with theory and applications
  • Model Validation and Selection: model validation and selection are orthogonal issues to previous technique; during the course the fundamentals are described and discussed (e.g., AIC, BIC, cross-validation, etc. ).

A detailed schedule of the course can be found here; topics are just indicative while days and teachers are corret "up to some last minute change".

Course Evaluation

The course evaluation is composed by two parts:

  • A homework with exercises covering the whole program that counts for 30% of the course grade
  • A oral examination covering the whole progran that count for 70% of the course grade

The homework is just one per year, it will be published at the end of the course and you will have 15 days to turn it in. It is not mandatory, however if you do not turn it in you loose 30% of the course grade. There is the option of substitute the homework with a practical project, but this has to be discussed and agreed with the course professor.

Teaching Material

In the following you can find the lecture slides used by the teacher and the teaching assistants during classes. Some additional material that could be used to prepare the oral examination is provided as well together with the homework.

Machine Learning and Pattern Recognition

Clustering

Dimensionality Reduction and Feature Selection

Homeworks

The homework, although not mandatory, counts for the 30% of the course grade (i.e., if you do not turn it in you loose 30% of the final grade). You have 15 days to turn it in to the teacher. This year the homework is due by the 5th of July! Please turn in a digital (or digitalized) copy of your homework.

Past years course homework; you can use them to make some practice and prepare this year homework ;-)

Additional Lecture Notes and Bibliography

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