Difference between revisions of "Information Retrieval and Data Mining"

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.

• 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

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:

• [2015] Course introduction: introductory slides of the course with useful information about the grading, and the course logistics.
• [2015] Data Mining: Data Mining introduction, historical perspective and related topics, the data mining process.
• [2015] Data Representation: Data representation types and issues, file formats and public datasets.
• [2015] Decision Trees: Recap on probability, condiational entropy and information gain, decision trees.
• [2015] Decision Trees Pruning: Decision trees and overfitting, subtree rising, and subtree replacement.
• [2015] Classification Rules: Classification rules, direct and indirect methods, decision tree pruning classification rules pruning.
• [2015] Learning Association Rules: Association rules, A-Priori algorithms, sequence mining and GSP algorithm.
• [2015] Markov Models: Markov Chains and PageRank
• [2015] Link Analysis: Link Analysis, centrality, PageRank

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

Additional Resources

Papers, links, and stuff useful to integrate the textbook

• [2015] Chi-Square: Chi-Square table to be used for the exercises

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:

• 08/07/2016 Exam
• 22/02/2016 Exam
• 05/02/2016 Exam

• 29/09/2015 Exam
• 15/09/2015 Exam
• 08/07/2015 Exam
• 25/02/2015 Exam
• 11/02/2015 Exam

Online Resources

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

* TBC