Scheda programma d'esame
INFORMATION RETRIEVAL
PAOLO FERRAGINA
Anno accademico2017/18
CdSINFORMATICA
Codice289AA
CFU6
PeriodoPrimo semestre
LinguaInglese

ModuliSettoreTipoOreDocente/i
INFORMATION RETRIEVALINF/01LEZIONI48
PAOLO FERRAGINA unimap
Learning outcomes
Knowledge

The student who successfully completes the course will have the ability to design a simple search engine and/or one of the numerous IR tools which are at the core of modern Web applications.

Assessment criteria of knowledge

The student will be assessed on his/her demonstrated ability to discuss the main course contents using the appropriate terminology.

Methods:

  • Final oral exam
  • Final written exam

Further information can be found at the home page of the course.

Skills

Students will be able to design and evaluate IR tools and search engines, by deploying the most adavanced algorithmic solutions to date.

Assessment criteria of skills

Via written and oral exam.

Behaviors

Students will be exposed to the context of IR tools and search engines, their challenges and algorithmic design choices. They'll appreciate the impacts in time and space of various known solutions, and be able to make their own choices and evaluate their pro/cons.

Assessment criteria of behaviors

Via written and oral exams

Prerequisites

Basics of Algorithms, Maths, Programming.

Co-requisites

Students should have attended (and passed!) either Algorihtm Engineering or Algorithm Design courses. 

Prerequisites for further study

It is desiderable that students have attended some data mining and machine learning courses

Teaching methods

Delivery: face to face

Learning activities:

  • attending lectures

Attendance: Advised

 

Syllabus

Study, design and analysis of IR systems which are efficient and effective to process, mine, search, cluster and classify documents, coming from textual as well as any unstructured domain. In the lectures, we will:

  • study and analyze the main components of a modern search engine: Crawler, Parser, Compressor, Indexer, Query resolver, Query and Document annotator, Results Ranker;
  • dig into some basic algorithmic techniques which are now ubiquitous in any IR application for data compression, indexing and sketching;
  • describe few other IR tools which are used either as a component of a search engine or as independent tools and build up the previous algorithmic techniques, such as: Classification, Clustering, Recommendation, Random Sampling, Locality Sensitive Hashing.

 

Bibliography

C.D. Manning, P. Raghavan, H. Schutze. Introduction to Information Retrieval. Cambridge University Press, 2008

Chapter 2 “Text compression” of Managing Gigabytes, I.H. Witten and A. Moffat and T.C. Bell, Morgan Kauffman, Second edition, 1999.

Notes provided by the teacher 

Ultimo aggiornamento 06/07/2017 11:47