PV211 Introduction to Information Retrieval

Fakulta informatiky
jaro 2018
Rozsah
2/1/0. 3 kr. (plus ukončení). Doporučované ukončení: zk. Jiná možná ukončení: k, z.
Vyučující
doc. RNDr. Petr Sojka, Ph.D. (přednášející)
RNDr. Vít Starý Novotný, Ph.D. (cvičící)
RNDr. Michal Růžička, Ph.D. (pomocník)
Garance
doc. RNDr. Petr Matula, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky
Rozvrh
St 12:00–13:50 D3, kromě St 16. 5.
  • Rozvrh seminárních/paralelních skupin:
PV211/01: Čt 8:00–8:50 B410, kromě Čt 22. 2., kromě Čt 12. 4., kromě Čt 19. 4., kromě Čt 10. 5. ; a Čt 22. 2. 8:00–8:50 B311, Čt 12. 4. 8:00–8:50 B311, Čt 19. 4. 8:00–8:50 B311, Čt 10. 5. 8:00–8:50 B311, P. Sojka, V. Starý Novotný
PV211/02: Čt 9:00–9:50 B410, kromě Čt 22. 2., kromě Čt 12. 4., kromě Čt 19. 4., kromě Čt 10. 5. ; a Čt 22. 2. 9:00–9:50 B311, Čt 12. 4. 9:00–9:50 B311, Čt 19. 4. 9:00–9:50 B311, Čt 10. 5. 9:00–9:50 B311, P. Sojka, V. Starý Novotný
Předpoklady
Curiosity and motivation to retrieve information about information retrieval. Chapters 1--5 benefit from basic course on algorithms and data structures. Chapters 6--7 needs 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 36 mateřských oborů, zobrazit
Cíle předmětu
Main objectives can be summarized as follows: - to understand basic principles of information retrieval: document preprocessing, indexing, and querying done on up to a web scale (as Google does); - to understand principles and algorithms of NLP-based text preprocessing, text semantic filtering and classification, and web searching needed for information systems and digital library design.
Výstupy z učení
Students that successfully complete the course will be able
- to understand architecture, algorithms and basic principles of indexing and searching of (textual) information systems;
- to evaluate the design and properties of information systems;
- to understand key algorithms (PageRank, kNN), metrics (precision, recall, F-measure) used in the information retrieval domain;
- to gain insight into inner working of scalable systems of Google type.
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 and MathML retrieval
  • Text classification with vector space model
  • Machine learning and information retrieval
  • Hierarchical clustering
  • Matrix decompositions and latent semantic indexing
  • Web search basics
  • Web crawling and indexes
  • Link analysis, PageRank
  • Invited lectures on related topics: image indexing, machine learning to rank, deep learning approaches, or even gait recognition.
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 (10 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 topically constrained discussion (during lecture hours) or by solving examples during exercises. This will respect individual learning speed and students' apriori knowledge. Questions on PV211 IS discussion forum are welcome especially before lectures.
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 will 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 semester and for active participation in the course.
Classification system is based on points achieved (100 pts). A student can get 50 pts during the term: 20 pts for each of two midterm tests, 10 pts for your activity during term (lectures or discussion forums,...), and 50 pts for the final test. Final written exam will consist of open exercises (30 pts, similar to midterm ones) and multiple choice questions (20 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. One substitute midterm test will be held during the exam period for those officially excused at the study department.
Vyučovací jazyk
Angličtina
Informace učitele
http://www.fi.muni.cz/~sojka/PV211/
All related info is posted and updated on https://www.fi.muni.cz/~sojka/PV211/
Další komentáře
Studijní materiály
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích jaro 2014, jaro 2015, jaro 2016, jaro 2017, jaro 2019, jaro 2020, jaro 2021, jaro 2022, jaro 2023, jaro 2024, jaro 2025.