Difference between revisions of "Information Retrieval and Data Mining"

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(Created page with "__FORCETOC__ The following are last minute news you should be aware of ;-) 09/10/2015: New edition of PAMI website is out, stay tuned! <!-- 15/07/2015: The Media:Grades_...")
 
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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!
+
  09/10/2015: First edition of IRDM website is out, stay tuned!
−
<!--
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15/07/2015: The [[Media:Grades_150706_PAMI.pdf |grades from the 06/07/2015 call]] are out.
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15/02/2015: The [[Media:Grades_150209_PAMI.pdf |grades from the 09/02/2015 call]] are out.
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09/01/2015: The 2015 homework is out!!
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14/11/2014: Updated the linear regression slides, and the detailed schedule
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09/11/2014: Change in the detailed schedule, Matteucci will be teaching instead of Eynard
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07/11/2014: Update of the full schedule for the course + the slides about regression + labs 1 to 3 material
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26/10/2014: Tomorrow 27/10/2014 PAMI will start at 15:15 due to a change in the schedule with IRDM
+
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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)!!!!
+
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23/09/2014: Scores from the 15/09/2014 written exam including the Homeworks are [[Media:Grades_140915_PAMI.pdf|published here]]!!
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24/07/2014: Scores from the 30/06/2014 written exam including the Homeworks are [[Media:Grades_140630_PAMI.pdf|published here]]!!
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21/03/2014: Scores from the 20/02/2014 written exam including the Homeworks are [[Media:Grades_140220_PAMI.pdf|published here]]!!
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18/02/2014: Scores from the 06/02/2014 written exam including the Homeworks are [[Media:Grades_140206_PAMI_HW.pdf|published here]]!!
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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!!
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04/01/2014: The third homework for the 2013-2014 course edition has been published!
+
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01/01/2014: Happy new year!!!
+
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29/11/2013: The second homework for the 2013-2014 course edition has been published!
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21/11/2013: Tomorrow, 22/11/2013, the lecture will be in room 4.1
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09/11/2013: New change of classroom ... all lectures will be in V.08
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            Update to the detailed schedule (swap between 06/12 and 13/12 teachers)
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28/10/2013: The first homework for the 2013-2014 course edition has been published!
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25/10/2013: new change in the classrooms! Monday in V.08 and Friday in V2.12
+
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14/10/2013: small change in the detailed schedule of the lectures
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11/10/2013: change of classroom due to limited space from V08 to VS9
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08/10/2013: final grades for the [[Media:Grades_130920_PAMI.pdf|20/09/2013 exam]]
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07/10/2013: a new edition of the course starts today!
+
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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)
+
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            final grades for the [[Media:Grades_Homeworks_130726_PAMI.pdf|homeworks at the date of 26/07/2013]]
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24/07/2013: final grades for the [[Media:Grades_130711_PAMI.pdf|11/07/2013 exam]]
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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]]
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21/06/2013: fixed third homework publication ...
+
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21/06/2013: the third homework is out! You have to turn it in before you take the exam (the first time).
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17/06/2013: bug fix in the homework part 2, the online pdf is now updated
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13/06/2013: update to the schedule with pointers to the relevant chapters
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09/05/2013: second homework is out -> deadline to turn it in Sunday 23/06/2013
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03/06/2013: no lecture on 11/06 it will be on 13/06/2013 from 15:15 to 18:15 in room V07
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            update to the detailed schedule with previous change
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            added file for Part 1 of Homework 1
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28/05/2013: fixed file prostate.data for the homework
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26/05/2013: first homework is out -> deadline to turn it in Sunday 09/06/2013
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21/05/2013: detailed schedule updated
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13/04/2013: change to the schedule to recover missed lecture
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05/03/2013: a new edition of the course starts today!
+
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05/03/2013: grades from the 29/01/2013 exam are available at [[Media:Grades_130129_PAMI.pdf|this link]]
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+
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05/10/2012: Grades for the 19/09/2012 exam are available at [[Media:Grades_120919_PAMI.pdf|this link]]
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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]]
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18/07/2012: Grades for the 10/07/2012 exam are available at [[Media:Grades_120710_PAMI.pdf|this link]]
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08/07/2012: Grades for the 26/06/2012 exam are available at [[Media:Grades_120626_PAMI.pdf|this link]]
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17/06/2012: Homework bugfix, check out below!
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12/06/2012: second and third (this one is optional) are out!
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11/06/2012: updated lecture schedule with details on 18/06 and 19/06 lectures
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02/06/2012: homework extension! The deadline is '''Tuesday 05/06/2012 23:59 CET'''!!
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26/05/2012: the first part of the homework is out! You have to turn it in by Sunday 3/6/2012!
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03/04/2012: published some material on "probability basics"
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12/03/2012: a new edition of the course starts today!
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-->
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==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://www.deib.polimi.it/ita/personale/dettagli/267262 Matteo Matteucci]: the course teacher
−
* [http://davide.eynard.it/ Davide Eynard]: the teaching assistant
+
* [http://www.deib.polimi.it/ita/personale/dettagli/705996 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 43:
  
 
  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 50:
 
|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 ||  
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
−
|19/10/2015 || Monday || - || - || - || No classes this week
+
|19/10/2015 || Monday || 15:15 - 18:15 || V.S8-B || Luca Bondi ||  
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
−
|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 || Linear Regression (Ch. 3 ISL)
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
−
|30/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Clustering
+
|30/11/2015 || Monday || -- || -- || -- || No Lecture Today
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
 
|07/12/2015 || Monday ||  -  || - || - || No classes this week
 
|07/12/2015 || Monday ||  -  || - || - || No classes this week
 
|-
 
|-
−
|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 ||  
 
|-
 
|-
−
|14/12/2014 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Classification
+
|14/12/2014 || Monday || 15:15 - 18:15 || V.S8-B || Luca Bondi ||  
 
|-
 
|-
−
|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 || 08:15 - 10:15 || V.S8-B || Matteo Matteucci ||  
 
|-
 
|-
−
|21/12/2014 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Classification
+
|21/12/2014 || Monday || 15:15 - 17:15 || V.S8-B || Luca Bondi ||
 +
|-
 +
|08/01/2016 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci ||
 +
|-
 +
|11/01/2016 || Monday || 15:15 - 18:15 || V.S8-B || Matteo Matteucci ||
 +
|-
 +
|15/01/2016 || 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
 
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**3.8 take a look, you can skip 3.8.3 to 3.8.6
 
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**3.9 take a look
 
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*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
 
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-->
 
  
 
===Course Evaluation===
 
===Course Evaluation===
  
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The '''new''' course evaluation is composed by two parts:
+
Course evaluation is through a written exam covering the whole program
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+
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* HW: Homework with exercises covering the whole program
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* WE: A written examination covering the whole program
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−
 
+
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the final score will take the '''MAXIMUM''' between '''WE''' and the combination '''0.7*WE + 0.3*HW'''. In practice
+
−
 
+
−
* the homework can only increase your score
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* the homework can only impact for the 30% of the score
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* the homework is not mandatory
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===Homeworks===
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====Homework 2015/2016====
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Not published yet ...
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====Homework 2014/2015====
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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:
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* 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
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* the deadline to turn the homework in is the first time you take the PAMI exam, we will grade it when grading your classwork
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* 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)
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* 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
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==Teaching Material (the textbook)==
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Lectures will be based on material taken from the book.
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* [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
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If you are interested in a more deep treatment of the topics you can refer to the following book from the same authors
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* [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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Some additional material that could be used to prepare the oral examination will be provided together with the past homeworks.
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==Teaching Material==
  
 
===Teacher Slides===
 
===Teacher Slides===
Line 276: Line 122:
 
* [[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.
 
* [[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.
  
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Exercising and Laboratories:
 
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* Lab 1: Introduction to R
 
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**[[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])
 
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**[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.
 
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**[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)
 
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* [[Media:Lab02.pdf | Lab2]]: Questions and exercises on Statistical Learning
 
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* [[Media:Lab03.pdf | Lab3]]: First exercises on linear regression
 
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<!--
 
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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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* [[Media:ProbabilityBasics.pdf | Probability Basics]]: Slides on probability basics used to introduce Statistical Decision Theory.
 
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* [[Media:PAMI_ModelSelection.pdf | Model Selection]]:  slides presenting images, tables and examples about model selection (taken from ''The Elements of Statistical Learning'' book).
 
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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 Resources===
 
===Additional Resources===
 
Papers and links useful to integrate the textbook
 
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.

Revision as of 02:30, 9 October 2015


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

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
16/10/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci
19/10/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi
23/10/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci
26/10/2015 Monday -- -- -- No Lecture Today
30/11/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci Linear Regression (Ch. 3 ISL)
02/11/2015 Monday -- -- -- No Lecture Today
06/11/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci
09/11/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi
13/11/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci
16/11/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi
20/11/2015 Friday -- -- -- No Lecture Today
23/11/2015 Monday 15:15 - 18:15 V.S8-B Luca Bondi
27/11/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci
30/11/2015 Monday -- -- -- No Lecture Today
04/12/2015 Friday 08:15 - 12:15 V.S8-B Matteo Matteucci
07/12/2015 Monday - - - No classes this week
11/12/2014 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci
14/12/2014 Monday 15:15 - 18:15 V.S8-B Luca Bondi
18/12/2015 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci
21/12/2014 Monday 15:15 - 17:15 V.S8-B Luca Bondi
08/01/2016 Friday 08:15 - 10:15 V.S8-B Matteo Matteucci
11/01/2016 Monday 15:15 - 18:15 V.S8-B Matteo Matteucci
15/01/2016 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

Teacher Slides

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

Lectures:

  • [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.
  • [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.)
  • [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.
  • [2014-2015] Linear Regression: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso.
  • [2014-2015] Linear Classification: From Linear Regression to Logistic Regression. Linear Discriminant Analysis and Quadratic Discriminant Analysis. Comparison between linear classification methods.
  • [2014-2015] Support Vector Machines: Discriminative vs. generative methids. Hyperplanes learning and Perceptron. Maximum Margin Classifiers. The Kernel trick and Support Vector Machines.


Additional Resources

Papers and links useful to integrate the textbook


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:

These are the text of past exams to give and idea on what to expect a theoretical questions:

Online Resources

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

  • 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)


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