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Before choosing courseDD2476 Search Engines and Information Retrieval Systems 9.0 creditsAdministrate About course

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

Choose semester and course offering

Choose semester and course offering to see information from the correct course syllabus and course offering.

* Retrieved from Course syllabus DD2476 (Spring 2019–)

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; structure of Web search engines.

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 Disposition

No information inserted

Literature and preparations

Specific prerequisites

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.

Equipment

No information inserted

Literature

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

Examination

  • LABA - Laboratory lessons, 6,0 hp, betygsskala: A, B, C, D, E, FX, F
  • LABB - Laboratory lessons, 3,0 hp, betygsskala: 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: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Profile picture Johan Boye

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 DD2476

Offered by

EECS/Intelligent Systems

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Contact

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

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex