Difference between revisions of "Information Retrieval and Data Mining"

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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 ;-)
−
  09/10/2015: New edition of PAMI website is out, stay tuned!
+
  06/10/2016: Grades from the 30/09/2016 exam can be found at this [[Media:Grades_160930_IRDM.pdf|link]]
−
<!--
+
  16/09/2016: Grades from the 12/09/2016 exam can be found at this [[Media:Grades_160912_IRDM.pdf|link]]
−
15/07/2015: The [[Media:Grades_150706_PAMI.pdf |grades from the 06/07/2015 call]] are out.
+
  02/08/2016: Updated the past exams list with exams from the 2015/2016 year
−
15/02/2015: The [[Media:Grades_150209_PAMI.pdf |grades from the 09/02/2015 call]] are out.
+
  31/07/2016: Grades from the 08/07/2016 exam can be found at this [[Media:Grades_160708_IRDM.pdf|link]]
−
  09/01/2015: The 2015 homework is out!!
+
  14/03/2016: Grades from the 22/02/2016 exam can be found at this [[Media:Grades_160222_IRDM.pdf|link]]
−
14/11/2014: Updated the linear regression slides, and the detailed schedule
+
  17/02/2016: Grades from the 05/02/2016 exam can be found at this [[Media:Grades_160205_IRDM.pdf|link]]
−
09/11/2014: Change in the detailed schedule, Matteucci will be teaching instead of Eynard
+
  18/01/2015: Schedule revised due to a cut & paste error :-(
−
07/11/2014: Update of the full schedule for the course + the slides about regression + labs 1 to 3 material
+
  18/01/2015: Schedule revised to avoid the overlap with Operational Research Course
−
26/10/2014: Tomorrow 27/10/2014 PAMI will start at 15:15 due to a change in the schedule with IRDM
+
  15/12/2015: Schedule revised until January (Note: on 18/01/2016 two extra hours are borrowed from PAMI)
−
10/10/2014: The new course edition is about to start!!! Stay tuned it will be significantly different from the past (check the '''new'' words in the text)!!!!
+
  09/12/2015: IRDM Exams for the Winter Calls will be on: 05/02/2016 and 22/02/2016
−
23/09/2014: Scores from the 15/09/2014 written exam including the Homeworks are [[Media:Grades_140915_PAMI.pdf|published here]]!!
+
  04/12/2015: Added slides on Decision Tree Pruning and Classification Rules
−
  24/07/2014: Scores from the 30/06/2014 written exam including the Homeworks are [[Media:Grades_140630_PAMI.pdf|published here]]!!
+
  24/11/2015: Moved from 21/12 to 14/12 a lecture by Luca Bondi
−
  21/03/2014: Scores from the 20/02/2014 written exam including the Homeworks are [[Media:Grades_140220_PAMI.pdf|published here]]!!
+
  10/11/2015: Moved from 14/12 to 30/11 a lecture by Luca Bondi
−
  18/02/2014: Scores from the 06/02/2014 written exam including the Homeworks are [[Media:Grades_140206_PAMI_HW.pdf|published here]]!!
+
  25/10/2015: Added slides on data representation and decision trees
−
  17/02/2014: Scores from the 06/02/2014 written exam are [[Media:Grades_140206_PAMI.pdf|published here]] ... in hours you will get homeworks as well!!
+
  11/10/2015: Added link to Teaching Assistant website to get his material
−
  04/01/2014: The third homework for the 2013-2014 course edition has been published!
+
  09/10/2015: First edition of IRDM website is out, stay tuned!
−
  01/01/2014: Happy new year!!!
+
−
29/11/2013: The second homework for the 2013-2014 course edition has been published!
+
−
  21/11/2013: Tomorrow, 22/11/2013, the lecture will be in room 4.1
+
−
09/11/2013: New change of classroom ... all lectures will be in V.08
+
−
            Update to the detailed schedule (swap between 06/12 and 13/12 teachers)
+
−
28/10/2013: The first homework for the 2013-2014 course edition has been published!
+
−
25/10/2013: new change in the classrooms! Monday in V.08 and Friday in V2.12
+
−
14/10/2013: small change in the detailed schedule of the lectures
+
−
11/10/2013: change of classroom due to limited space from V08 to VS9
+
−
  08/10/2013: final grades for the [[Media:Grades_130920_PAMI.pdf|20/09/2013 exam]]
+
−
07/10/2013: a new edition of the course starts today!
+
−
21/08/2013: final grades for the [[Media:Grades_130726_PAMI.pdf|26/07/2013 exam]] (one grade is incomplete for technical reasons, it will be fixed soon)
+
−
            final grades for the [[Media:Grades_Homeworks_130726_PAMI.pdf|homeworks at the date of 26/07/2013]]
+
−
24/07/2013: final grades for the [[Media:Grades_130711_PAMI.pdf|11/07/2013 exam]]
+
−
  22/07/2013: at the following link you can find the grades for the [[Media:Grades_20130722_PAMI.pdf|homeworks and the 11/07/2013 exam]]
+
−
  21/06/2013: fixed third homework publication ...
+
−
21/06/2013: the third homework is out! You have to turn it in before you take the exam (the first time).
+
−
17/06/2013: bug fix in the homework part 2, the online pdf is now updated
+
−
  13/06/2013: update to the schedule with pointers to the relevant chapters
+
−
09/05/2013: second homework is out -> deadline to turn it in Sunday 23/06/2013
+
−
03/06/2013: no lecture on 11/06 it will be on 13/06/2013 from 15:15 to 18:15 in room V07
+
−
            update to the detailed schedule with previous change
+
−
            added file for Part 1 of Homework 1
+
−
28/05/2013: fixed file prostate.data for the homework
+
−
26/05/2013: first homework is out -> deadline to turn it in Sunday 09/06/2013
+
−
21/05/2013: detailed schedule updated
+
−
13/04/2013: change to the schedule to recover missed lecture
+
−
  05/03/2013: a new edition of the course starts today!
+
−
05/03/2013: grades from the 29/01/2013 exam are available at [[Media:Grades_130129_PAMI.pdf|this link]]
+
−
 
+
−
05/10/2012: Grades for the 19/09/2012 exam are available at [[Media:Grades_120919_PAMI.pdf|this link]]
+
−
21/07/2012: Updated grades for the 10/07/2012 and 26/06/2012 exams are available at [[Media:Grades_120710_PAMI.pdf|this link]]
+
−
  18/07/2012: Grades for the 10/07/2012 exam are available at [[Media:Grades_120710_PAMI.pdf|this link]]
+
−
08/07/2012: Grades for the 26/06/2012 exam are available at [[Media:Grades_120626_PAMI.pdf|this link]]
+
−
17/06/2012: Homework bugfix, check out below!
+
−
12/06/2012: second and third (this one is optional) are out!
+
−
11/06/2012: updated lecture schedule with details on 18/06 and 19/06 lectures
+
−
02/06/2012: homework extension! The deadline is '''Tuesday 05/06/2012 23:59 CET'''!!
+
−
26/05/2012: the first part of the homework is out! You have to turn it in by Sunday 3/6/2012!
+
−
03/04/2012: published some material on "probability basics"
+
−
  12/03/2012: a new edition of the course starts today!
+
−
-->
+
  
 
==Course Aim & Organization==
 
==Course Aim & Organization==
  
−
The objective of this course is to give an advanced presentation, i.e., a statistical perspective, of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling.
+
The course covers tools and systems adopted to handle big data, e.g., large collections of textual data. In the first part, the course focuses on the analysis of information embedded in large collections, using tools that range from decision trees, classification rules, association rules, graph-based link analysis. The second part of the course covers the efficient retrieval of information, discussing the algorithms and data structures adopted to enable answering keyword based queries, as well as indexing methods to enable fast search.  
−
 
+
 
===Teachers===
 
===Teachers===
  
 
The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.
 
The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.
  
−
* [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the course teacher
+
* [http://chrome.ws.dei.polimi.it/ Matteo Matteucci]: the course teacher
−
* [http://davide.eynard.it/ Davide Eynard]: the teaching assistant
+
* [http://home.deib.polimi.it/lbondi Luca Bondi]: the teaching assistant
  
 
===Course Program===
 
===Course Program===
  
−
Techniques from machine and statistical learning are presented from a theoretical (i.e., statistics and information theory) and practical perspective through the descriptions of algorithms, the theory behind them, their implementation issues, and few examples from real applications. The course follows, at least partially, the following '''new''' book which is available for download in pdf
+
The course outline is:
  
−
* [http://www-bcf.usc.edu/~gareth/ISL/ An Introduction to Statistical Learning with Applications in R] by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
+
*Data mining
−
 
+
**The Data Mining process
−
The course is composed by a set of ex-cattedra lectures on specific techniques (e.g., linear regression, linear discriminant analysis, clustering, etc.). Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is:
+
**Decision Trees and Decision Rules
−
 
+
**Rule Induction Methods
−
* '''''Machine Learning and Pattern Classification''''': the general concepts of Machine Learning and Patter Recognition are introduced with a brief review of statistical decision theory;
+
**Association Rules
−
* '''''Linear Classification Techniques''''': linear methods for classification will be presented as the starting point (e.g., Linera Regression on the indicator matrix, Linear and Quadratic Discriminant Analysis, Logistic Regression, Percptron rule and Optimal Separating Hyperplanes, a.k.a., Support Vector Machines)
+
**Frequent Itemset Analysis
−
* '''''Linear Regression Techniques''''': linear methods for regression will be disccussed and compared (e.g., Linear Regression, Ridge Regression, Lasso, LARS).
+
*Web information retrieval
−
* '''''Unsupervised Learning Techniques''''': the most common approaches to unsupervised learning are described mostly focusing on clustering techniques such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, Jarvis-Patrick, etc.;
+
**Web modelling and crawling
−
* '''''Model Validation and Selection''''': model validation and selection are orthogonal issues to all previous techniques; during the course their fundamentals are described and discussed (e.g., AIC, BIC, cross-validation, etc. ).
+
**Graph-based retrieval models (PageRank, HITS)
 +
*Text-based information retrieval
 +
*IR models (Boolean models, vector space models, probabilistic models)
 +
**Evaluation of IR systems
 +
**Text processing
 +
**Advanced IR models (Latent Semantic Indexing)
 +
*Indexing
 +
**Inverted indexing
 +
**Multidimensional indexing
 +
**Rank aggregation
  
 
===Detailed course schedule===
 
===Detailed course schedule===
Line 91: Line 58:
  
 
  Note: Lecture timetable interpretation
 
  Note: Lecture timetable interpretation
−
  * On Mondays, in V.08, starts at 13:30 (quarto d'ora accademico), ends at 15:15
+
  * On Mondays, in V.08, starts at 15:30 (quarto d'ora accademico), ends at 18:15
−
  * On Fridays, in V.08, starts at 10:30 (quarto d'ora accademico), ends at 12:15 or 13:15 (check!)
+
  * On Fridays, in V.08, starts at 08:30 (quarto d'ora accademico), ends at 10:15
  
 
{| border="1" align="center" style="text-align:center;"
 
{| border="1" align="center" style="text-align:center;"
Line 98: Line 65:
 
|Date || Day || Time || Room || Teacher || Topic
 
|Date || Day || Time || Room || Teacher || Topic
 
|-
 
|-
−
|05/10/2015 || Monday || 13:15 - 15:15 || V08 || Matteo Matteucci || Course Introduction (Ch. 1 ISL)
+
|05/10/2015 || Monday || 15:15 - 18:15 || V08 || Matteo Matteucci || Course Introduction
 
|-
 
|-
−
|09/10/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
+
|09/10/2015 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || The Data Mining Process
 
|-
 
|-
−
|12/10/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Introduction to R (Ch. 2 ISL)
+
|12/10/2015 || Monday || 15:15 - 18:15 || V.S8-B || Luca Bondi || Introduction to Information Retrieval
 
|-
 
|-
−
|16/10/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
+
|16/10/2015 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Data representation and coding
 
|-
 
|-
−
|19/10/2015 || Monday || - || - || - || No classes this week
+
|19/10/2015 || Monday || 15:15 - 18:15 || V.S8-B || Luca Bondi || Basic models for Information Retrieval
 
|-
 
|-
−
|23/10/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Simple Linear Regression (Ch. 2 ISL)
+
|23/10/2015 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Introduction to probability
 
|-
 
|-
−
|26/10/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Statistical Decision Theory Exercises (Ch. 2 ISL)
+
|26/10/2015 || Monday || -- || -- || -- || No Lecture Today
 
|-
 
|-
−
|30/11/2015 || Friday || 10:15 - 12:15 || V.S8-B || Matteo Matteucci || Linear Regression (Ch. 3 ISL)
+
|30/11/2015 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Entropy and information gain
 
|-
 
|-
−
|02/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Simple Linear Regression (Ch. 3 ISL)
+
|02/11/2015 || Monday || -- || -- || -- || No Lecture Today
 
|-
 
|-
−
|06/11/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
+
|06/11/2015 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Decision Trees
 
|-
 
|-
−
|09/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Linear Regression and Feature Selection
+
|09/11/2015 || Monday || 15:15 - 18:15 || V.S8-B || Luca Bondi || Evaluation of IR systems and Text processing
 
|-
 
|-
−
|13/11/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Feature Selection and Shrinkage in Linear Regression (Ch. 6 ISL)
+
|13/11/2015 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Decision Trees
 
|-
 
|-
−
|16/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Intro Clustering
+
|16/11/2015 || Monday || 15:15 - 18:15 || V.S8-B || Luca Bondi || Math background and Advanced IR models
 
|-
 
|-
−
|20/11/2015 || Friday || 10:15 - 12:15 || V.S8-B || Davide Eynard || Clustering with exercises
+
|20/11/2015 || Friday || -- || -- || -- || No Lecture Today
 
|-
 
|-
−
|23/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Clustering Advanced
+
|23/11/2015 || Monday || 15:15 - 18:15 || V.S8-B || Luca Bondi || Advanced IR models, Inverted indexing
 
|-
 
|-
−
|27/11/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
+
|27/11/2015 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Classification rules
 
|-
 
|-
−
|30/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Clustering
+
|30/11/2015 || Monday || 15:15 - 18:15 || V.S8-B || Luca Bondi || Multidimentional indexing
 
|-
 
|-
−
|04/12/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Classification by Linear Discriminant Analysis (Ch. 4 ISL)
+
|04/12/2015 || Friday || 08:15 - 12:15 || V.S8-B || Matteo Matteucci || Sequential covering algorithm
 
|-
 
|-
−
|07/12/2015 || Monday ||  -  || - || - || No classes this week
+
|07/12/2015 || Monday ||  -  || - || - || No Lecture Today
 
|-
 
|-
−
|11/12/2014 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
+
|11/12/2014 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Rule pruning by Chi-Square test
 
|-
 
|-
−
|14/12/2014 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Classification
+
|14/12/2014 || Monday || 15:15 - 17:15 || V.S8-B || Luca Bondi || Rank aggregation
 
|-
 
|-
−
|18/12/2015 || Friday || 10:15 - 12:15 || V.S8-B || Matteo Matteucci || Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
+
|18/12/2015 || Friday || - || - || - || No Lecture Today
 
|-
 
|-
−
|21/12/2014 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Classification
+
|21/12/2014 || Monday || - || - || - || No Lecture Today
 +
|-
 +
|08/01/2016 || Friday || - || - || - || No Lecture Today
 +
|-
 +
|11/01/2016 || Monday || 15:15 - 18:15 || V.S8-B || Matteo Matteucci || Frequent pattern mining and association rules
 +
|-
 +
|15/01/2016 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || A-priori algorithm and sequential patterns mining
 +
|-
 +
|18/01/2016 || Monday || 15:15 - 17:15 || V.S8-B || Matteo Matteucci || Markov chains
 +
|-
 +
|22/01/2016 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci ||  
 
|-
 
|-
 
|}
 
|}
−
 
−
Chapters are intended as complete except for
 
−
* Ch.4 ESL: Section 4.5
 
−
* Ch.12 ESL: Sections 12.1, 12.2, 12.3
 
−
* Ch.9 ISL: Sections 9.1, 9.2, 9.3
 
−
 
−
<!---
 
−
|13/10/2014 || Monday || 15:15 - 18:15 || V08 || Matteo Matteucci || Course Introduction (Ch. 1 ISL)
 
−
|17/10/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
 
−
|20/10/2014 || Monday || 13:15 - 15:15 || V08 || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
 
−
|24/10/2014 || Friday || 10:30 - 12:15 || V08 || Davide Eynard || Introduction to R (Ch. 2 ISL)
 
−
|27/10/2014 || Monday || 15:15 - 17:15 || V08 || Davide Eynard || Statistical Decision Theory Exercises (Ch. 2 ISL)
 
−
|31/10/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Simple Linear Regression (Ch. 2 ISL)
 
−
|03/11/2014 || Monday || 13:30 - 15:15 || V08 || Davide Eynard || Exercises on Simple Linear Regression (Ch. 3 ISL)
 
−
|07/11/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Linear Regression (Ch. 3 ISL)
 
−
|10/11/2014 || Monday || 13:30 - 15:15 || V08 || Matteo Matteucci || Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
 
−
|14/11/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Feature Selection and Shrinkage in Linear Regression (Ch. 6 ISL)
 
−
|17/11/2014 || Monday || 13:30 - 15:15 || V08 || Davide Eynard || Exercises on Linear Regression and Feature Selection
 
−
|21/11/2014 || Friday || 10:30 - 12:15 || V08 || Matteo Matteucci || Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
 
−
|24/11/2014 || Monday ||  -  || - || - || No classes this week
 
−
|28/11/2014 || Friday ||  -  || - || - || No classes this week
 
−
|01/12/2014 || Monday || 13:30 - 15:15 || V08 || Davide Eynard || Exercises on Classification
 
−
|05/12/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Classification by Linear Discriminant Analysis (Ch. 4 ISL)
 
−
|12/12/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
 
−
|15/12/2014 || Monday || 13:10 - 15:00 || V08 || Davide Eynard || Exercises on Classification
 
−
| -  || - || - || - || - || Holidays
 
−
|09/01/2015 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
 
−
|12/01/2015 || Monday || 13:10 - 15:00 || V08 || Davide Eynard || Intro Clustering
 
−
|16/01/2015 || Friday || 10:30 - 12:15 || V08 || Davide Eynard || Clustering with exercises
 
−
|19/01/2015 || Monday || 13:10 - 15:00 || V08 || Davide Eynard || Clustering Advanced
 
−
|23/01/2015 || Friday || 10:30 - 12:15 || V08 || Davide Eynard || Exercises on Clustering
 
−
 
−
|21/10/2013 || Monday || 13:15 - 15:00 || VS9 || Matteo Matteucci || Linear Regression on the Indicator Matrix (Ch. 4.3)
 
−
|25/10/2013 || Friday || 10:30 - 13:15 || VS9 || Matteo Matteucci || Linear Discriminant Analysis (Ch. 4.3)
 
−
|28/10/2013 || Monday || 13:15 - 15:00 || V08 || Matteo Matteucci || Regularized Linear Discriminant Analysis, LDA in the (K-1) subspace (Ch. 4.3)
 
−
|04/11/2013 || Monday || 13:15 - 15:15 || V08 || Matteo Matteucci || Fisher Projection - Logistic Regression (Ch. 4.4)
 
−
|08/11/2013 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Logistic Regression (Ch. 4.4)
 
−
|11/11/2013 || Monday || 13:15 - 15:00 || V08 || Luigi Malagò    ||  Linear Regression Methods (Ch. 2, Ch. 3, (*))
 
−
|15/11/2013 || Friday || 10:30 - 13:15 || V08 || Luigi Malagò    ||  Linear Regression Methods (Ch. 2, Ch. 3, (*))
 
−
|18/11/2013 || Monday || 13:15 - 15:00 || V08 || Luigi Malagò    ||  Linear Regression Methods (Ch. 2, Ch. 3, (*))
 
−
|22/11/2013 || Friday || 10:30 - 13:15 || 4.1 || Luigi Malagò    ||  Linear Regression Methods (Ch. 2, Ch. 3, (*))
 
−
|25/11/2013 || Monday || 13:15 - 15:00 || V08 || Davide Eynard    ||  Clustering I: Introduction and K-Means
 
−
|29/11/2013 || Friday || 10:30 - 13:15 || V08 || Davide Eynard    ||  Clustering II: K-Means Alternatives, Hierarchical, SOM
 
−
|02/12/2013 || Monday || 13:15 - 15:00 || V08 || Davide Eynard    ||  Clustering III: Mixture of Gaussians, DBSCAN, Jarvis-Patrick
 
−
|06/12/2013 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Perceptron Learning and Maximum Margin Classifiers (Ch.4.5.2)
 
−
|13/12/2013 || Friday || 10:30 - 13:15 || V08 || Davide Eynard    ||  Clustering IV: Spectral Clustering and Evaluation Measures
 
−
|16/12/2013 || Monday || 13:15 - 15:00 || V08 || Matteo Matteucci || Support Vector Marchines (Ch.12.1, 12.2, 12.3)
 
−
|20/12/2013 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Kernel Smoothing (Ch.6.1) and Kernel Density Estimation (Ch.6.6, Ch.6.9)
 
−
 
−
Kernel Smoothing Methods and Kerned Density Estimation (Ch.6.1,
 
−
Gaussian Mixture Models (Ch.6.8) and the EM Algorithm (Ch.8.5)
 
−
Decision Trees ([[Media:PAMI_DTnR.pdf |handout]] + Ch. 9.2)
 
−
Perceptron Learning and Support Vector Machines (Ch 4.5)
 
−
Maximum margin classification (Ch. 4.5.2)
 
−
Support Vector Machines (Ch. 12.1, Ch. 12.2, Ch. 12.3.0, Ch. 12.3.1 + SVM paper)
 
−
Model Selection (Ch. 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.10,)
 
−
Linear regression methods (Ch 2.1, 2.2, 2.3 and 2.3.1, 2.4, 2.6 and 2.6.1, 2.7 and 2.7.1, 2.8 and 2.8.1, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.9,  see paper "Least Angle Regression" linked below, pages 1-16)
 
−
 
−
(*) With respect to following version of the book [http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf PDF file of book (10th printing with corrections, Jan 2013)]
 
−
*Chapter 2 is a good introductory chapter.
 
−
**in particular refer to 2.3.1 for an introduction to least squares
 
−
**section 2.4 for an introduction to statistical decision theory
 
−
**section 2.5 for an example and 2.9 for a discussion about bias and variance decomposition of prediction error
 
−
**section 2.8 for introduction to restricted (penalized) estimators
 
−
*Chapter 3 is the most important chapter for linear regression
 
−
**3.1
 
−
**3.2 (you can skip confidence internals for \beta, but study the Z-scores)
 
−
**3.2.2 the proof is not requires, the statement of the theorem is important
 
−
**3.2.3 you can skip the regression by successive orthogonalizations, however dont skip the first part of the section
 
−
**3.2.4 skip this section
 
−
**3.3 very important
 
−
**3.4 very important (i will not ask the formulas for the degrees of freedom of ridge and lasso)
 
−
**3.5 you can skip this
 
−
**3.6 take a look
 
−
**3.7 you can skip this
 
−
**3.8 take a look, you can skip 3.8.3 to 3.8.6
 
−
**3.9 take a look
 
−
*As to the paper [http://www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf Least Angle Regression] take a look at section 1 to 3
 
−
-->
 
  
 
===Course Evaluation===
 
===Course Evaluation===
  
−
The '''new''' course evaluation is composed by two parts:
+
Course evaluation is through a written exam covering the whole program
  
−
* HW: Homework with exercises covering the whole program
+
==Teaching Material==
−
* WE: A written examination covering the whole program
+
  
−
the final score will take the '''MAXIMUM''' between '''WE''' and the combination '''0.7*WE + 0.3*HW'''. In practice
+
===Teachers Slides===
  
−
* the homework can only increase your score
+
In the following you can find the lecture slides used by the teacher and the teaching assistants during classes.
−
* the homework can only impact for the 30% of the score
+
−
* the homework is not mandatory
+
  
−
===Homeworks===
+
Teacher Lectures:
 +
* [[Media:IRDM2015-00-Intro.pdf | [2015] Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics.
 +
* [[Media:IRDM2015-01-DataMining.pdf | [2015] Data Mining]]: Data Mining introduction, historical perspective and related topics, the data mining process.
 +
* [[Media:IRDM2015-02-DataRepresentation.pdf | [2015] Data Representation]]: Data representation types and issues, file formats and public datasets.
 +
* [[Media:IRDM2015-03-DecisionTrees.pdf | [2015] Decision Trees]]: Recap on probability, condiational entropy and information gain, decision trees.
 +
* [[Media:IRDM2015-04-DecisionTreesPruning.pdf | [2015] Decision Trees Pruning]]: Decision trees and overfitting, subtree rising, and subtree replacement.
 +
* [[Media:IRDM2015-05-ClassificationRules.pdf | [2015] Classification Rules]]: Classification rules, direct and indirect methods, decision tree pruning classification rules pruning.
 +
* [[Media:IRDM2015-06-AssociationRulesBasics.pdf | [2015] Learning Association Rules]]: Association rules, A-Priori algorithms, sequence mining and GSP algorithm.
 +
* [[Media:IRDM2015-07-MarkovModels.pdf | [2015] Markov Models]]: Markov Chains and PageRank
 +
* [[Media:IRDM2015-08-LinkAnalysis.pdf | [2015] Link Analysis]]: Link Analysis, centrality, PageRank
  
−
====Homework 2015/2016====
 
  
−
Not published yet ...
 
  
−
====Homework 2014/2015====
+
Teaching Assistand Lectures can be found directly on [http://home.deib.polimi.it/lbondi/irdm-201516/index.html Luca Bondi website].
  
−
We have published the [http://davide.eynard.it/2015/01/05/statistical-learning-with-r-introduction-and-setup/ Homework 2014/2015]. Please keep in mind:
+
===Additional Resources===
−
* the homework is not meant to get more grade, it is intended for you to understand better, i.e., from a practical perspective too, the topics of the course
+
Papers, links, and stuff useful to integrate the textbook
−
* the deadline to turn the homework in is the first time you take the PAMI exam, we will grade it when grading your classwork
+
−
* you can make the homework in groups up to 2/3 people, the deadline to turn it in is the date the first person in the groups takes the exam (and it will be graded for all members in the group at that call)
+
−
* provided you attended the lab lectures, and you have the R environment set up, the homework should require not more than 1 day per part
+
  
−
==Teaching Material (the textbook)==
+
* [[Media:chisquared.png | [2015] Chi-Square]]: Chi-Square table to be used for the exercises
−
 
+
−
Lectures will be based on material taken from the book.
+
−
 
+
−
* [http://www-bcf.usc.edu/~gareth/ISL/ An Introduction to Statistical Learning with Applications in R] by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
+
−
 
+
−
If you are interested in a more deep treatment of the topics you can refer to the following book from the same authors
+
−
 
+
−
* [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.
+
−
 
+
−
Some additional material that could be used to prepare the oral examination will be provided together with the past homeworks.
+
−
 
+
−
===Teacher Slides===
+
−
 
+
−
In the following you can find the lecture slides used by the teacher and the teaching assistants during classes.
+
−
 
+
−
Lectures:
+
−
* [[Media:PAMI2015-01-Intro.pdf | [2015] Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised and unsupervised learning. Regression, classification, clustering terminology and examples.
+
−
* [[Media:PAMI2014-02-StatisticalLearning.pdf | [2014-2015] Statistical Learning Introduction]]: Statistical Learning definition, rationale, and trade-offs (e.g., prediction vs. inference, parametric vs non parametric models, flexibility vs. interpretability, etc.)
+
−
* [[Media:PAMI2014-03-AssessingModelAccuracy.pdf | [2014-2015] Statistical Learning and Model Assessment]]: Model Assessment for Regression and Classification, Bias-Variance trade-off, Model complexity and overfitting, K-Nearest Neighbors Classifier vs. Bayes Classifier.
+
−
* [[Media:PAMI2014-04-LinearRegression.pdf | [2014-2015] Linear Regression]]: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso.
+
−
* [[Media:PAMI2014-05-LinearClassification.pdf | [2014-2015] Linear Classification]]: From Linear Regression to Logistic Regression. Linear Discriminant Analysis and Quadratic Discriminant Analysis. Comparison between linear classification methods.
+
−
* [[Media:PAMI2014-06-SupportVectorMachines.pdf | [2014-2015] Support Vector Machines]]: Discriminative vs. generative methids. Hyperplanes learning and Perceptron. Maximum Margin Classifiers. The Kernel trick and Support Vector Machines.
+
−
 
+
−
 
+
−
Exercising and Laboratories:
+
−
* Lab 1: Introduction to R
+
−
**[[Media:BasicsofR.txt | Basics of R]]: the list of commands ran in Lab 01. Note that the list is heavily based on the Lab in Section 2.3 of the book (you can find the original [http://www-bcf.usc.edu/~gareth/ISL/code.html here]), but I preferred to integrate it with some additional hints from my personal experience and other sources such as [http://www.pitt.edu/~njc23/ this one])
+
−
**[http://www.statlearning.com/ Statistical Learning]: the website of the Introduction to Statistical Learning book. In the [http://www-bcf.usc.edu/~gareth/ISL/data.html Data Sets and Figures] page you will also find links to download the Auto.data and Auto.cvs datasets we used during the Lab.
+
−
**[http://cran.r-project.org/ The Comprehensive R Archive Network]: the place where you can download R and its packages (note that the book often refers to ISLR and MASS packages, it is good for you to install them soon)
+
−
* [[Media:Lab02.pdf | Lab2]]: Questions and exercises on Statistical Learning
+
−
* [[Media:Lab03.pdf | Lab3]]: First exercises on linear regression
+
  
 
<!--
 
<!--
−
* [[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).
 
−
* [[Media:ProbabilityBasics.pdf | Probability Basics]]: Slides on probability basics used to introduce Statistical Decision Theory.
 
−
* [[Media:PAMI_ModelSelection.pdf | Model Selection]]:  slides presenting images, tables and examples about model selection (taken from ''The Elements of Statistical Learning'' book).
 
−
* [[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).
 
−
* [[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).
 
−
* [[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.
 
−
* [[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).
 
−
-->
 
−
 
−
===Additional Resources===
 
−
Papers and links useful to integrate the textbook
 
−
 
 
* [http://scott.fortmann-roe.com/docs/BiasVariance.html Bias vs. Variance]: "Understanding the Bias-Variance Tradeoff" essay by Scott Fortmann-Roe
 
* [http://scott.fortmann-roe.com/docs/BiasVariance.html Bias vs. Variance]: "Understanding the Bias-Variance Tradeoff" essay by Scott Fortmann-Roe
 
* ...
 
* ...
−
<!--
 
 
* 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.
 
* 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.
 
* 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.
 
* 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.
 
* ...  
 
* ...  
−
-->
 
−
 
−
<!--
 
−
===Clustering Slides===
 
−
These are the slides used to present clustering algorithms during lectures
 
−
 
−
* Lesson 1: Introduction to Clustering and K-Means ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e1.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e1.pdf handouts])
 
−
 
−
* Lesson 2: K-Means alternatives, Hierarchical, SOM ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e2.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e2.pdf handouts])
 
−
 
−
* Lesson 3: Mixture of Gaussians, DBSCAN, Jarvis-Patrick ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e3.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e3.pdf handouts])
 
−
 
−
* Lesson 4: Evaluation measures ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e4.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e4.pdf handouts]) and Spectral Clustering ([http://davide.eynard.it/teaching/2012_PAMI/Spectral%20Clustering.pdf])
 
−
 
 
-->
 
-->
  
 
===Past Exams and Sample Questions===
 
===Past Exams and Sample Questions===
−
Since 2014/2015 the course was changed and the exams format as well. For this edition of the course you should expect '''2 theoretical questions + 2 practical exercises''' (on average). Some examples from the past year can be found here:
+
Since 2015/2016 the course has changed teacher, this might have an impact on the exams format as well. Some examples from the past year can be found here, please expect differences:
  
−
* [[Media:2015_02_09_PAMI.pdf |09/02/2015 Exam]]
+
* [[Media:2016_07_08_IRDM.pdf |08/07/2016 Exam]]
−
* [[Media:2015_02_23_PAMI.pdf |23/02/2015 Exam]]
+
* [[Media:2016_02_22_IRDM.pdf |22/02/2016 Exam]]
−
* [[Media:2015_06_07_PAMI.pdf |07/06/2015 Exam]]
+
* [[Media:2016_02_05_IRDM.pdf |05/02/2016 Exam]]
−
* [[Media:2015_09_14_PAMI.pdf |14/09/2015 Exam]]
+
−
* [[Media:2015_09_30_PAMI.pdf |30/09/2015 Exam]]
+
  
−
These are the text of past exams to give and idea on what to expect a theoretical questions:
+
* [[Media:2015_09_29_IRDM.pdf |29/09/2015 Exam]]
−
 
+
* [[Media:2015_09_15_IRDM.pdf |15/09/2015 Exam]]
−
* [[Media:2013_09_20_PAMI.pdf |20/09/2013 Exam]]
+
* [[Media:2015_07_08_IRDM.pdf |08/07/2015 Exam]]
−
* [[Media:2013_09_10_PAMI.pdf |10/09/2013 Exam]]
+
* [[Media:2015_02_25_IRDM.pdf |25/02/2015 Exam]]
−
* [[Media:2013_07_26_PAMI.pdf |26/07/2013 Exam]]
+
* [[Media:2015_02_11_IRDM.pdf |11/02/2015 Exam]]
−
* [[Media:2013_07_11_PAMI.pdf |11/07/2013 Exam]]
+
−
* [[Media:2013_01_29_PAMI.pdf |29/01/2013 Exam]]
+
−
* [[Media:2012_09_19_PAMI.pdf |19/09/2012 Exam]]
+
−
* [[Media:2012_09_04_PAMI.pdf |04/09/2012 Exam]]
+
−
* [[Media:2012_07_10_PAMI.pdf |10/07/2012 Exam]]
+
−
* [[Media:2012_06_26_PAMI.pdf |26/06/2012 Exam]]
+
−
* [[Media:2012_02_03_PAMI.pdf |03/02/2012 Exam]]
+
−
* [[Media:2011_09_19_PAMI.pdf |19/09/2011 Exam]]
+
−
* [[Media:2011_09_08_PAMI.pdf |08/09/2011 Exam]]
+
−
* [[Media:2011_07_15_PAMI.pdf |15/07/2011 Exam]]
+
−
* [[Media:2011_06_29_PAMI.pdf |29/06/2011 Exam]]
+
  
 
===Online Resources===
 
===Online Resources===
Line 350: Line 178:
 
The following are links to online sources which might be useful to complement the material above
 
The following are links to online sources which might be useful to complement the material above
  
−
* [http://math.arizona.edu/~hzhang/math574m.html MATH 574M] University of Arizona Course on ''Statistical Machine Learning and Data Mining''; here you can find slides covering part of the course topics (the reference book for this course is again ''The Elements of Statistical Learning'')
+
  * TBC
−
 
+
−
<!--
+
−
== 2013-2014 Homework ==
+
−
 
+
−
The 2013 Homework (alike the 2012 one) is organized as an octave series of tutorials. You are requested to go through the tutorials and practice with the algorithms that have been presented in class. To prove us you have done it and that you have understood the code you will be requested to solve few exercises and provide us a pdf report by email
+
−
 
+
−
=== Part 1: Linear Classification Methods ===
+
−
 
+
−
* [[Media:homework_pami_classification_2013_2014.pdf | Homework 2013-2014 on Classification]]: this is the text of the first part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade and the deadline to submit the solution by Sunday 17/11 23:59'''
+
−
** [[Media:SAheart.data | SAheart.data]]: the dataset used for the homework
+
−
** [[Media:SAheart.info | SAheart.info]]: the dataset used for the homework
+
−
 
+
−
'''Note 1:''' Submit the solution by loading it on www.dropitto.me/matteucci (pwd is dropittome)
+
−
 
+
−
'''Note 2:''' please name your pdf as pami_SURNAME_STUDENTID_classification.pdf; if you submit a homework for different people, please pick one of the names for the file but PUT ALL THE NAMES IN THE COVER PAGE!!
+
−
 
+
−
=== Part 2: Regression ===
+
−
* [[Media:homework_pami_regression_2013_2014.pdf | Homework 2013-2014 Regression]]: this is the text of the second part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade and the deadline to submit the solution by email to malago@di.unimi.it (cc to matteo.matteucci@polimi.it) is Friday 20/12 23:59'''
+
−
** [[Media:prostate.data | prostate.data]]: the dataset used for the homework
+
−
** [[Media:prostate.info | prostate.info]]: the dataset used for the homework
+
−
** [[Media:diabete.mat | diabete.mat]]: the dataset used for the homework
+
−
** [[Media:textread.m | textread.m]]: (optional) function which might be useful depending on your octave version
+
−
** [[Media:strread.m | strread.m]]: (optional) function which might be useful depending on your octave version
+
−
 
+
−
For any question or doubt please sen us an email as soon as possible.
+
−
 
+
−
'''Note 1:''' for some strange reason the CSM of the website has decided to rename the files with capitals, please save them in lower case :-(
+
−
 
+
−
<strike>'''Note 2:''' rename the file Diabete.data into diabete.mat ... still fighting with the CSM :-)</strike>
+
−
 
+
−
'''Note 3:''' the code has been tested with octave under linux, we suggest to use it not to spend too much time with installing it under windows or using matlab. If you do not have linux installed, try using a live CD as the ubuntu 13.04 live distro ;-)
+
−
 
+
−
=== Part 3: Clustering ===
+
−
 
+
−
The code and the text of the third part of the homework are available online at this post
+
−
 
+
−
* [http://davide.eynard.it/2013/12/30/octave-clustering-demo-part-6-more-evaluation/ Homework 2013-2014 on clustering evaluation]
+
−
 
+
−
As usual, '''this part of the homework will contribute to the 10% of the grade'''; the deadline to submit the solution is the end of the course. You have to '''send it to davide.eynard_at_gmail.com Friday 24/01 23:59'''.
+
−
 
+
−
'''Note 1:''' for any doubt or question send an email, as soon as possible, to Davide Eynard so to have a prompt reply and not get stuck during homework execution.
+
−
 
+
−
'''Note 2:''' you have to turn in only the solution of "Ocatave clustering demo part 6", while the other parts can be used as reference to improve your understanding in basic clustering algorithms.
+
−
 
+
−
=== Part 2: Classification ===
+
−
 
+
−
* [[Media:homework_pami_classification_2013.pdf | Homework 2013 Classification]]: this is the text of the second part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade and the deadline to submit the solution by Sunday 23/06 23:59'''
+
−
** [[Media:SAheart.data | SAheart.data]]: the dataset used for the homework
+
−
** [[Media:SAheart.info | SAheart.info]]: the dataset used for the homework
+
−
 
+
−
'''Note 1:''' Submit the solution by loading it on www.dropitto.me/matteucci (pwd is dropittome)
+
−
 
+
−
'''Note 2:''' please name your pdf as pami_SURNAME_STUDENTID_classification.pdf; if you submit a homework for different people, please pick one of the names for the file but PUT ALL THE NAMES IN THE COVER PAGE!!
+
−
 
+
−
 
+
−
 
+
−
'''Errata Corrige''': there were a few bugs in the homework text. I have updated the pdf and they were:
+
−
 
+
−
In the computation of feature projection, the code for the maximization of a'B*a via SVD should be changed as it follows
+
−
  % maximization of a'*B*a / a'*w*a via SVD
+
−
[Vw, Dw, Vw] = svd(W);
+
−
Whalf = Vw * sqrt(Dw) * Vw'; % Whalf'*Whalf == W
+
−
Wminushalf = inv(Whalf);
+
−
Mstar = M*Wminushalf;
+
−
    % Add this variable for computing Mstar mean
+
−
    meanMstar = mean(Mstar);
+
−
for i=1:size(M,1)
+
−
    % Remove the mean saved before the loop
+
−
    Mstar(i,:) = Mstar(i,:)-meanMstar;
+
−
end
+
−
Bstar = Mstar'*Mstar;
+
−
[Vstar, Db, Vstar] = svd(Bstar);
+
−
 
+
−
In the Fisher projection it is more correct to use only the training data to learn the projection and then we can train and test on the corresponding subsets
+
−
 
+
−
a = FisherProjection(X(training,:),Y(training,:));
+
−
reducedX = X*a(:,1);
+
−
[mu_0, mu_1, sigma, p_0, p_1] = linearDiscriminantAnalysis_train(reducedX(training), Y(training))
+
−
 
+
−
I forgot to filter for just the training samples when performing Quadratic Discriminant Analysis
+
−
 
+
−
quadX = expandToQuadraticSpace(X);
+
−
%check this out!
+
−
size(quadX)
+
−
beta = linearRegression_train(quadX(training), Y(training));
+
−
 
+
−
And in general you should always train on the training data and test on the testing data ;-).
+
−
 
+
−
=== Part 3: Clustering ===
+
−
 
+
−
The code and the text of the third part of the homework are available online at these posts
+
−
 
+
−
* [http://davide.eynard.it/2013/06/18/octave-clustering-demo-part-4-k-medoids/ Homework 2013 on k-medoids]
+
−
* [http://davide.eynard.it/2013/06/18/octave-clustering-demo-part-5-hierarchical-clustering/ Homework 2013 on hierarchical clustering]
+
−
 
+
−
As usual, '''this part of the homework will contribute to the 10% of the grade'''; the deadline to submit the solution is '''before the you take the exam''' sending it to davide.eynard_at_gmail.com.
+
−
 
+
−
* [http://davide.eynard.it/2012/06/05/octave-clustering-demo-part-0-introduction-and-setup/ Homework 2012 part 3:] follow this tutorial and answer the questions from all 5 sub-tutorials.
+
−
 
+
−
== 2012 Homework ==
+
−
 
+
−
 
+
−
The Homework of 2012 organized like an octave/matlab series of tutorials. You are requested to go through the tutorials and practice with the algorithms that have been presented in class. To prove us you have done it and that you have understood the code you will be requested to solve few exercises and provide us a pdf report by email
+
−
 
+
−
* [[Media:PAMI_homework_2012_1.pdf | Homework 2012 part 1]]: this is the text of the first part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade and the deadline to submit the solution by email to matteucci@elet.polimi.it and malago@elet.polimi.it is Tuesday 5/6 23:59'''
+
−
** [[Media:prostate.data | prostate.data]]: the dataset used for the homework
+
−
** [[Media:prostate.info | prostate.info]]: the dataset used for the homework
+
−
** [[Media:textread.m | textread.m]]: (optional) function which might be useful depending on your octave version
+
−
** [[Media:strread.m | strread.m]]: (optional) function which might be useful depending on your octave version
+
−
 
+
−
'''Note:''' for some strange reason the CSM of the website has decided to rename the files with capitals, please save them in lower case :-(
+
−
 
+
−
* [[Media:PAMI_homework_2012_2.pdf | Homework 2012 part 2]]: this is the text of the second part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade; the deadline to submit the solution by email to matteucci@elet.polimi.it is the day before the exam you decide to attend''' (e.g., if you decide to take the exam on the 26/6 then you need to turn it in by 25/6).
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** [[Media:SAheart.data | SAheart.data]]: the dataset used for the homework
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** [[Media:SAheart.info | SAheart.info]]: the dataset used for the homework
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'''Errata Corrige''': there were a few bugs a bug in the homework text. I have updated the pdf and they were:
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In the code for loading the data I forgot to remove the first column which you do not need
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data = dlmread('SAheart.data',',',1,1);
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X = data(:,1:9);
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Y = data(:,10);
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In the StratifiedSampling function the sorted verctors should be assigned
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% just an ahestetic sorting
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testing = sort(testing);
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training = sort(training);
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In the computation of feature projection, the code for the maximization of a'B*a via SVD should be changed as it follows
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% maximization of a'*B*a / a'*w*a via SVD
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[Vw, Dw, Vw] = svd(W);
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Whalf = Vw * sqrt(Dw) * Vw'; % Whalf'*Whalf == W
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Wminushalf = inv(Whalf);
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Mstar = M*Wminushalf;
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    % Add this variable for computing Mstar mean
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    meanMstar = mean(Mstar);
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for i=1:size(M,1)
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    % Remove the mean saved before the loop
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    Mstar(i,:) = Mstar(i,:)-meanMstar;
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end
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Bstar = Mstar'*Mstar;
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[Vstar, Db, Vstar] = svd(Bstar);
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In the expansion to quadratic space the starting index for the inner loop should i and not 1. Moreover in some cases it might be possible to have columns which are duplicated (e.g., with boolean attribute); in this case you should not need the robust version of linear regression.
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function extendedX = expandToQuadraticSpace(X)
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    % adds new columns to extendedX; keeps X for other calculations
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    extendedX = X;
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    for i=1:size(X, 2)
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        for j=i:size(X, 2)
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            newColumn = X(:, i) .* X(:, j);
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            extendedX = [extendedX newColumn];
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        end
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    end
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    % remove duplicated columns
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    duplicates = [];
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    for i=1:size(extendedX, 2)
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        for j=i+1:size(extendedX, 2)
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            if(sum(extendedX(:,i)==extendedX(:,j)) == size(X,1))
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                duplicates = [duplicates j];
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            end
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        end
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    end
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    extendedX(:,duplicates) = [];
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end
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* [http://davide.eynard.it/2012/06/05/octave-clustering-demo-part-0-introduction-and-setup/ Homework 2012 part 3]: the third part of the homework is '''optional''', so you are not required to complete it. However, if you want to give it a try and use it to understand the topics covered by Davide Eynard in his lectures you are welcome. As usual, the questions in this homework are very close to the ones you will find in classworks, so we suggest to have a look at hose anyway! '''In case you decide to turn it in and have it contribute with a 10% to the grade, the deadline to submit the solution by email to matteucci@elet.polimi.it and davide.eynard@polimi.it is the day before you decide to take the exam''' (e.g., if you decide to take the exam on the 10/7 then you need to turn it in by 9/7)
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'''Note:''' homeworks are meant to let you see (and practice) a little bit with the topics presented during the course. They are evaluated because you spent some time on those and thus you deserve some credit for that ;-)
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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 08:20, 6 October 2016


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

06/10/2016: Grades from the 30/09/2016 exam can be found at this link
16/09/2016: Grades from the 12/09/2016 exam can be found at this link
02/08/2016: Updated the past exams list with exams from the 2015/2016 year
31/07/2016: Grades from the 08/07/2016 exam can be found at this link
14/03/2016: Grades from the 22/02/2016 exam can be found at this link
17/02/2016: Grades from the 05/02/2016 exam can be found at this link
18/01/2015: Schedule revised due to a cut & paste error :-(
18/01/2015: Schedule revised to avoid the overlap with Operational Research Course
15/12/2015: Schedule revised until January (Note: on 18/01/2016 two extra hours are borrowed from PAMI)
09/12/2015: IRDM Exams for the Winter Calls will be on: 05/02/2016 and 22/02/2016
04/12/2015: Added slides on Decision Tree Pruning and Classification Rules
24/11/2015: Moved from 21/12 to 14/12 a lecture by Luca Bondi 
10/11/2015: Moved from 14/12 to 30/11 a lecture by Luca Bondi 
25/10/2015: Added slides on data representation and decision trees
11/10/2015: Added link to Teaching Assistant website to get his material
09/10/2015: First edition of IRDM website is out, stay tuned!

Course Aim & Organization

The course covers tools and systems adopted to handle big data, e.g., large collections of textual data. In the first part, the course focuses on the analysis of information embedded in large collections, using tools that range from decision trees, classification rules, association rules, graph-based link analysis. The second part of the course covers the efficient retrieval of information, discussing the algorithms and data structures adopted to enable answering keyword based queries, as well as indexing methods to enable fast search.

Teachers

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

Course Program

The course outline is:

  • Data mining
    • The Data Mining process
    • Decision Trees and Decision Rules
    • Rule Induction Methods
    • Association Rules
    • Frequent Itemset Analysis
  • Web information retrieval
    • Web modelling and crawling
    • Graph-based retrieval models (PageRank, HITS)
  • Text-based information retrieval
  • IR models (Boolean models, vector space models, probabilistic models)
    • Evaluation of IR systems
    • Text processing
    • Advanced IR models (Latent Semantic Indexing)
  • Indexing
    • Inverted indexing
    • Multidimensional indexing
    • Rank aggregation

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 (I will notify you by email). Please note that not all days we have lectures!! 

Note: Lecture timetable interpretation
* On Mondays, in V.08, starts at 15:30 (quarto d'ora accademico), ends at 18:15
* On Fridays, in V.08, starts at 08:30 (quarto d'ora accademico), ends at 10:15
Date Day Time Room Teacher Topic
05/10/2015 Monday 15:15 - 18:15 V08 Matteo Matteucci Course Introduction
09/10/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci The Data Mining Process
12/10/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi Introduction to Information Retrieval
16/10/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci Data representation and coding
19/10/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi Basic models for Information Retrieval
23/10/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci Introduction to probability
26/10/2015 Monday -- -- -- No Lecture Today
30/11/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci Entropy and information gain
02/11/2015 Monday -- -- -- No Lecture Today
06/11/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci Decision Trees
09/11/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi Evaluation of IR systems and Text processing
13/11/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci Decision Trees
16/11/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi Math background and Advanced IR models
20/11/2015 Friday -- -- -- No Lecture Today
23/11/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi Advanced IR models, Inverted indexing
27/11/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci Classification rules
30/11/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi Multidimentional indexing
04/12/2015 Friday 08:15 - 12:15 V.S8-B Matteo Matteucci Sequential covering algorithm
07/12/2015 Monday - - - No Lecture Today
11/12/2014 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci Rule pruning by Chi-Square test
14/12/2014 Monday 15:15 - 17:15 V.S8-B Luca Bondi Rank aggregation
18/12/2015 Friday - - - No Lecture Today
21/12/2014 Monday - - - No Lecture Today
08/01/2016 Friday - - - No Lecture Today
11/01/2016 Monday 15:15 - 18:15 V.S8-B Matteo Matteucci Frequent pattern mining and association rules
15/01/2016 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci A-priori algorithm and sequential patterns mining
18/01/2016 Monday 15:15 - 17:15 V.S8-B Matteo Matteucci Markov chains
22/01/2016 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci

Course Evaluation

Course evaluation is through a written exam covering the whole program

Teaching Material

Teachers Slides

In the following you can find the lecture slides used by the teacher and the teaching assistants during classes.

Teacher Lectures:


Teaching Assistand Lectures can be found directly on Luca Bondi website.

Additional Resources

Papers, links, and stuff useful to integrate the textbook


Past Exams and Sample Questions

Since 2015/2016 the course has changed teacher, this might have an impact on the exams format as well. Some examples from the past year can be found here, please expect differences:

Online Resources

The following are links to online sources which might be useful to complement the material above 

* TBC
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