PV211 Introduction to Information Retrieval

Faculty of Informatics
Spring 2014
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: k (colloquium). Other types of completion: z (credit).
Teacher(s)
doc. RNDr. Petr Sojka, Ph.D. (lecturer)
RNDr. Martin Líška (seminar tutor)
RNDr. Tomáš Effenberger, Ph.D. (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
Thu 16:00–17:50 D3
  • Timetable of Seminar Groups:
PV211/T01: Tue 11. 3. to Sun 18. 5. each odd Tuesday 18:00–19:40 Učebna S1 (36a), M. Líška, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
PV211/01: each even Thursday 18:00–19:50 D3, M. Líška
PV211/02: each odd Thursday 18:00–19:50 D3, M. Líška
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
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 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, 2008, xxi, 482. ISBN 9780521865715. info
    recommended literature
  • http://informationretrieval.com/
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
Czech
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 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2014, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2014/PV211