PV211 Introduction to Information Retrieval

Faculty of Informatics
Spring 2016
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)
RNDr. Michal Balážia, Ph.D. (seminar tutor)
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
Tue 8:00–9:50 D2
  • Timetable of Seminar Groups:
PV211/01: Tue 10:00–10:50 B311, M. Balážia
PV211/02: Tue 11:00–11:50 B311, M. Balážia
Prerequisites
Interest 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.
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 36 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, 2008, xxi, 482. ISBN 9780521865715. 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, 2011, xxx, 913. ISBN 9780321416919. info
Teaching methods
Contact teaching will in addition to classic ex catedra lectures contain invited lectures of specialist from the IR (researchers of Seznam, a.s.), and eventually, support of autonomy learning (support of MOOC in Khan Academy style) -- flipped learning.
Assessment methods
Evaluation is based on the system that motivates students for continuous work during semester (50 points for ten short tests or homeworks assigned on almost every exercise). Final exam will allow getting another 50 points and will test understanding and abilities for effective information retrieval.
Language of instruction
English
Further comments (probably available only in Czech)
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 2015, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2016, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2016/PV211