PV211 Introduction to Information Retrieval

Fakulta informatiky
jaro 2022
Rozsah
2/1/0. 3 kr. (plus ukončení). Doporučované ukončení: zk. Jiná možná ukončení: k, z.
Vyučováno online.
Vyučující
doc. RNDr. Petr Sojka, Ph.D. (přednášející)
RNDr. Vít Novotný (cvičící)
Mgr. Dávid Lupták (cvičící)
Mgr. Michal Štefánik (cvičící)
Garance
doc. RNDr. Petr Sojka, Ph.D.
Katedra vizuální informatiky - Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky - Fakulta informatiky
Předpoklady
Curiosity and motivation to retrieve information about information retrieval. Chapters 1--5 benefit from a basic course on algorithms and data structures. Chapters 6--7 need in addition linear algebra, vectors, and dot products. For Chapters 11--13 basic probability notions are needed. Chapters 18--21 demand course in linear algebra, notions of matrix rank, eigenvalues, and eigenvectors.
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
předmět má 73 mateřských oborů, zobrazit
Cíle předmětu
The main objectives of this course are to introduce principles of information retrieval and get acquainted with machine learning algorithms for NLP-based text processing.
Výstupy z učení
Students will understand document preprocessing, tokenization, lemmatization, indexing, and querying done on up to a web-scale (as Google does). First principles and algorithms of NLP-based text preprocessing, text semantic filtering and classification, and web searching needed for information systems and digital library design will be taught.
Osnova
  • Boolean retrieval; The term vocabulary and postings lists
  • Dictionaries and tolerant retrieval
  • Index construction, Index compression
  • Scoring, term weighting, and the vector space model
  • Computing scores in a complete search system
  • Evaluation in information retrieval
  • Relevance feedback and query expansion
  • XML/MathML retrieval
  • Text classification with vector space model
  • Machine learning and information retrieval
  • Matrix decompositions and latent semantic indexing
  • Web search basics
  • Web crawling and indexes
  • Link analysis, PageRank
  • Invited lectures on hot topics, e.g. deep learning approaches to NLP.
Literatura
    povinná literatura
  • MANNING, Christopher D., Prabhakar RAGHAVAN a Hinrich SCHÜTZE. Introduction to information retrieval. 1st pub. Cambridge: Cambridge University Press, 2008. xxi, 482. ISBN 9780521865715. info
  • http://informationretrieval.org
    doporučená literatura
  • BAEZA-YATES, R. a Berthier de Araújo Neto RIBEIRO. Modern information retrieval : the concepts and technology behind search. 2nd ed. Harlow: Pearson, 2011. xxx, 913. ISBN 9780321416919. info
Výukové metody
Student activities explicitly welcomed as a part of evaluation (8 pts). Mentoring rather than ex cathedra lectures: ``The flipped classroom is a pedagogical model in which the typical lecture and homework elements of a course are reversed.'' Students will be expected to come prepared by reading given materials in advance. Contact hours will be devoted to a topically constrained discussion or by solving examples during exercises. This will respect individual learning speed and students' apriori knowledge. Rich study materials are available: MOOC, materials on http://web.stanford.edu/class/cs276/, including the whole IIR book http://nlp.stanford.edu/IR-book/.
These teaching methods may be complemented by invited lectures of specialists from the IR community (researchers of Seznam, Facebook, RaRe Technologies, etc.).
Metody hodnocení
Evaluation is based on the system that motivates students for continuous work during the semester and for active participation in the course.
The classification system is based on points achieved (100 pts). A student can get 60 pts during the term: 20 pts for each of two programming tasks, 12 pts for evaluation of your colleague's results, 8 pts for your activity during the term (lectures or discussion forums,...). 40 pts could be achieved in the final test (ROPOT in IS), consisting of multiple-choice questions (2x20 pts). In addition, one can get additional premium points based on activities during lectures, exercises (good answers) or negotiated related projects. Grading scale (adjustments based on ECTS suggestions) z/k[/E/D/C/B/A] corresponds approximately to 50/57/[64/71/78/85/92] points.
Dates of at least three terms of final exams will be announced via IS.muni.cz.
Vyučovací jazyk
Angličtina
Informace učitele
https://www.fi.muni.cz/~sojka/PV211/
Materials will be posted and updated in the interactive syllabi https://is.muni.cz/auth/el/fi/jaro2021/PV211/index.qwarp (and on https://www.fi.muni.cz/~sojka/PV211/).
Další komentáře
Předmět je vyučován každoročně.
Výuka probíhá každý týden.
Předmět je zařazen také v obdobích jaro 2014, jaro 2015, jaro 2016, jaro 2017, jaro 2018, jaro 2019, jaro 2020, jaro 2021.