Skip to main content

DD2475 Information Retrieval 9.0 credits

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

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus DD2475 (Autumn 2010–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

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; XML, structure of Web search engines; clustering, classification; spectral methods, random indexing; data mining.

Intended learning outcomes

After completing the course you will be able to:

  • Explain the concepts of indexing, vocabulary, normalization and dictionary in Information Retrieval
  • Define a boolean model and a vector space model, and explain the differences between them
  • Explain the differences between classification and clustering
  • Discuss the differences between different classification and clustering methods
  • Choose a suitable classification or clustering method depending on the problem constraints at hand
  • Implement classification in a boolean model and a vector space model
  • Implement a basic clustering method
  • Give account of a basic spectral method
  • Evaluate information retrieval algorithms, and give an account of the difficulties of evaluation
  • Explain the basics of XML and Web search.

Course disposition

No information inserted

Literature and preparations

Specific prerequisites

Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology. English B, or equivalent.

Recommended prerequisites

A level in Mathematics corresponding to at least 30 credits, including courses in Linear Algebra, Calculus in one and several variables, Mathematical Statistics, and a level in Computer Science corresponding to at least 15 credits. It is also beneficial to have taken courses in Machine Learning, Artificial intelligence, Language Engineering and/or Database Technology.


No information inserted


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

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

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


  • LAB1 - Laboratory Works, 3.0 credits, grading scale: P, F
  • LAB2 - Project, 3.0 credits, grading scale: A, B, C, D, E, FX, F
  • TEN1 - Exam, 3.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see:

Other requirements for final grade

The students participating in the course are expected to take part in all activities in the course with a particular emphasis on the exercises and laboratories. In addition the course focuses on training:
    * independently acquiring knowledge
    * oral and written presentation
Examination by one written exam (TEN1; 3.0 credits), laboratory assignments (LAB1; 3.0 credits), and a project assigment assessed orally and in writing (LAB2; 3.0 credits).

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted


Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DD2475

Offered by

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Supplementary information

This course is replaced by DD2476 Search Engines and Information Retrieval Systems from the year 11/12.