CdSINFORMATICA
Codice289AA
CFU6
PeriodoPrimo semestre
LinguaInglese
| Moduli | Settore/i | Tipo | Ore | Docente/i | |
| INFORMATION RETRIEVAL | INF/01 | LEZIONI | 48 |
|
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.
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.
Students will be able to design and evaluate IR tools and search engines, by deploying the most adavanced algorithmic solutions to date.
Via written and oral exam.
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.
Via written and oral exams
Basics of Algorithms, Maths, Programming.
Students should have attended (and passed!) either Algorihtm Engineering or Algorithm Design courses.
It is desiderable that students have attended some data mining and machine learning courses
Delivery: face to face
Learning activities:
- attending lectures
Attendance: Advised
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.
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
http://didawiki.di.unipi.it/doku.php/magistraleinformatica/ir/start