Difference between revisions of "Information Retrieval and Data Mining"

Revision as of 01:42, 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.

Matteo Matteucci: the course teacher
Luca Bondi: the 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 Lecture Today
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.
[2014-2015] Data Mining: Data Mining introduction, historical perspective and related topics, the Data Mining process.

Additional Resources

Papers and links useful to integrate the textbook

* TBC

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

Difference between revisions of "Information Retrieval and Data Mining"

Revision as of 01:42, 9 October 2015

Contents

Course Aim & Organization

Teachers

Course Program

Detailed course schedule

Course Evaluation

Teaching Material

Teacher Slides

Additional Resources

Past Exams and Sample Questions

Online Resources

Navigation menu

Personal tools

Namespaces

Variants

Views

More

Search

Matt's_Home_Page|Home Page

Master Courses Teaching

PhD Teaching

Other Info

Past Teaching

Chrome:About|About Chrome

Tools

@@ Line 115: / Line 115: @@
 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:IRDM2015-00-Intro.pdf | [2015] Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics.
-* [[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:IRDM2015-01-DataMining.pdf | [2014-2015] Data Mining]]: Data Mining introduction, historical perspective and related topics, the Data Mining process.
-* [[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.
 ===Additional Resources===
 Papers and links useful to integrate the textbook
+ * TBC
 <!--
@@ Line 132: / Line 129: @@
 * 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===
-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:2015_02_09_IRDM.pdf |09/02/2015 Exam]]
-* [[Media:2015_02_23_PAMI.pdf |23/02/2015 Exam]]
+* [[Media:2015_02_25_IRDM.pdf |25/02/2015 Exam]]
-* [[Media:2015_06_07_PAMI.pdf |07/06/2015 Exam]]
+* [[Media:2015_07_08_IRDM.pdf |08/07/2015 Exam]]
-* [[Media:2015_09_14_PAMI.pdf |14/09/2015 Exam]]
+* [[Media:2015_09_15_IRDM.pdf |15/09/2015 Exam]]
-* [[Media:2015_09_30_PAMI.pdf |30/09/2015 Exam]]
+* [[Media:2015_09_29_IRDM.pdf |29/09/2015 Exam]]
-These are the text of past exams to give and idea on what to expect a theoretical questions:
-* [[Media:2013_09_20_PAMI.pdf |20/09/2013 Exam]]
-* [[Media:2013_09_10_PAMI.pdf |10/09/2013 Exam]]
-* [[Media:2013_07_26_PAMI.pdf |26/07/2013 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===
@@ Line 178: / Line 144: @@
 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).
-** [[Media:SAheart.data | SAheart.data]]: the dataset used for the homework
-** [[Media:SAheart.info | SAheart.info]]: the dataset used for the homework
-'''Errata Corrige''': there were a few bugs a bug in the homework text. I have updated the pdf and they were:
-In the code for loading the data I forgot to remove the first column which you do not need
- data = dlmread('SAheart.data',',',1,1);
- X = data(:,1:9);
- Y = data(:,10);
-In the StratifiedSampling function the sorted verctors should be assigned
- % just an ahestetic sorting
- testing = sort(testing);
- training = sort(training);
-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 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.
- function extendedX = expandToQuadraticSpace(X)
-    % adds new columns to extendedX; keeps X for other calculations
-    extendedX = X;
-    for i=1:size(X, 2)
-        for j=i:size(X, 2)
-            newColumn = X(:, i) .* X(:, j);
-            extendedX = [extendedX newColumn];
-        end
-    end
-    % remove duplicated columns
-    duplicates = [];
-    for i=1:size(extendedX, 2)
-        for j=i+1:size(extendedX, 2)
-            if(sum(extendedX(:,i)==extendedX(:,j)) == size(X,1))
-                duplicates = [duplicates j];
-            end
-        end
-    end
-    extendedX(:,duplicates) = [];
- end
-* [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)
-'''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 ;-)
-== 2011 Homework ==
-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.
-* [[Media:PAMI_homework_2011_v02.pdf | Homework 2011 v02]] a minor change in the signature of the logistic regression function
-* [[Media:PAMI_homework_2011_v01.pdf | Homework 2011 v01]] text with questions and exercises
-* [[Media:dataset.txt | Dataset]] for the clustering exercise in csv format
-'''Frequently Asked Questions'''
-* '''''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].
-* '''''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]
-* '''''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).
-* '''''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.
-* '''''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 ;-)
-* '''''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.
-* '''''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!
--->