DD2476 Search Engines and Information Retrieval Systems 9.0 credits

Sökmotorer och informationssökningssystem

Please note

The information on this page is based on a course syllabus that is not yet valid.

A course in computer science focusing on basic theory, models, and methods for information retrieval.

  • Education cycle

    Second cycle
  • Main field of study

    Computer Science and Engineering
  • Grading scale

    A, B, C, D, E, FX, F

Course offerings

Spring 19 ir18 for programme students

Spring 19 ir19 for Study Abroad Programme (SAP)

  • Periods

    Spring 19 P3 (6.0 credits), P4 (3.0 credits)

  • Application code


  • Start date


  • End date


  • Language of instruction


  • Campus

    KTH Campus

  • Tutoring time


  • Form of study


  • Number of places

    No limitation

  • Schedule

    Schedule (new window)

  • Course responsible

    Johan Boye <jboye@kth.se>

  • Teacher

    Viggo Kann <viggo@kth.se>

  • Target group

    Only open för students within Study Abroad Programme (SAP)

  • Application

    Apply for this course at antagning.se through this application link.
    Please note that you need to log in at antagning.se to finalize your application.

Spring 20 ir20 for programme students

Intended learning outcomes

After completing the course you will be able to:

*  explain the concepts of indexing, vocabulary, normalization and dictionary in Information Retrieval,

*  give an account of different text similarity measures, and select a similarity measure suitable for the problem at hand,

*  define a boolean model and a vector space model, and explain the differences between them,

*  implement a method for ranked retrieval of a very large number of documents with hyperlinks between them,

*  evaluate information retrieval algorithms, and give an account of the difficulties of evaluation,

*  give an account of the structure of a Web search engine.

Course main content

Basic and advanced techniques for information systems: information extraction; efficient text indexing; indexing of non-text data; Boolean and vector space retrieval models; evaluation and interface issues; structure of Web search engines.


SF1604 Linear algebra,  SF1901 Probability theory and statistics,  DD1338 Algorithms and Data Structures, or corresponding courses.

Recommended prerequisites

A level in Mathematics corresponding to at least 30 credits, including courses in Linear Algebra and Mathematical Statistics, and a level in Computer Science corresponding to at least 15 credits.


C. D. Manning, P. Raghavan and H. Schütze: Introduction to Information Retrieval, Cambridge University Press, 2008.


  • LABA - Laboratory lessons, 6.0, grading scale: A, B, C, D, E, FX, F
  • LABB - Laboratory lessons, 3.0, grading scale: A, B, C, D, E, FX, F

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.

Offered by

EECS/Intelligent Systems


Johan Boye, e-post: jboye@kth.se


Johan Boye <jboye@kth.se>


Course syllabus valid from: Spring 2019.
Examination information valid from: Spring 2019.