Difference between revisions of "Information Retrieval and Data Mining"

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|04/12/2015 || Friday || 08:15 - 12:15 || V.S8-B || Matteo Matteucci ||  
 
|04/12/2015 || Friday || 08:15 - 12:15 || V.S8-B || Matteo Matteucci ||  
 
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|07/12/2015 || Monday ||  -  || - || - || No classes this week
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|07/12/2015 || Monday ||  -  || - || - || No Lecture Today
 
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|11/12/2014 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci ||  
 
|11/12/2014 || Friday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci ||  

Revision as of 01:36, 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 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. 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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