PV211 Introduction to Information Retrieval

Faculty of Informatics
Spring 2015
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Petr Sojka, Ph.D. (lecturer)
doc. RNDr. Petr Sojka, Ph.D. (seminar tutor)
RNDr. Tomáš Effenberger, Ph.D. (assistant)
RNDr. Martin Líška (assistant)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. Petr Sojka, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Wed 8:00–9:50 D3
  • Timetable of Seminar Groups:
PV211/01: Wed 10:00–10:50 D3, P. Sojka
PV211/02: Wed 11:00–11:50 D3, P. Sojka
Prerequisites
Interest and motivation to retrieve information about information retrieval.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 35 fields of study the course is directly associated with, display
Course objectives
Main objectives can be summarized as follows: - to understand basics of principles of information retrieval based on (XML) text processing and natural language understanding; - to understand principles and algorithms of NLP-based text preprocessing, text semantic filtering and classification, and web searching needed for textual information systems and digital library design.
Syllabus
  • 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
  • Probabilistic information retrieval
  • Language models for information 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
Literature
    required literature
  • MANNING, Christopher D., Prabhakar RAGHAVAN and Hinrich SCHÜTZE. Introduction to information retrieval. 1st pub. Cambridge: Cambridge University Press. xxi, 482. ISBN 9780521865715. 2008. info
  • http://informationretrieval.org
    recommended literature
  • BAEZA-YATES, R. and Berthier de Araújo Neto RIBEIRO. Modern information retrieval : the concepts and technology behind search. 2nd ed. Harlow: Pearson. xxx, 913. ISBN 9780321416919. 2011. info
Teaching methods (in Czech)
Kontaktní výuka bude kromě klasických přednášek obsahovat podporu autonomního učení studentů (výuková videa ve stylu Khan Academy, MOOC) -- tzv. `flipped learning'.
Assessment methods (in Czech)
Bodový hodnotící systém motivující studenta pro průběžnou autonomní práci v semestru (prémiové body). Závěrečné kolokvium -- písemný test testující získané znalosti a dovednosti při vyhledávání znalostí.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/~sojka/PV211/
The course is also listed under the following terms Spring 2014, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.
  • Enrolment Statistics (Spring 2015, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2015/PV211