Basic and advanced technologies for information retrieval: information extraction; efficient text indexing; indexing of non-textual data; boolean models and vector space models for search; evaluation and user interface issues; the structure of Internet search engines.
DD2476 Search Engines and Information Retrieval Systems 9.0 credits
This course has been discontinued.
Last planned examination: Spring 2024
Decision to discontinue this course:
The course is discontinued at the expiration of spring term 2024 in accordance with Head of School decision:: J-2022-0856.
Decision date: 07/06/2022
The course was given for the last time during the spring semester 2021. Final opportunity for examination in the course will be given spring term 2024.
The assessment modules LABA on 6 higher education credits and lab on 3 higher education credits can up to the spring semester 2024 be examined after contact with the examiner. Students are offered no teaching.
A course in computer science focusing on basic theory, models, and methods for information retrieval.
Information per course offering
Course offerings are missing for current or upcoming semesters.
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus DD2476 (Spring 2022–)Headings with content from the Course syllabus DD2476 (Spring 2022–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
On completion of the course, you should be able to:
* explain the concepts indexing, vocabulary, normalisation and dictionary in information retrieval,
* give an account of different distance measures for text and choose a distance measure that is appropriate for a given problem,
* define a boolean model and a vector space model and explain the difference between them,
* implement a method for ranked search of a very large number of documents with hyper links,
* evaluate information retrieval algorithms and give an account of difficulties with evaluation,
* give an account of the structure of an Internet search engine.
Literature and preparations
Specific prerequisites
SF1604 Linear Algebra, SF1901 Basic Statistics and Probability Theory, DD1338 Algorithms and Data Structures or equivalent 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
No information inserted
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 credits, grading scale: A, B, C, D, E, FX, F
- LABB - Laboratory lessons, 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.
Opportunity to complete the requirements via supplementary examination
No information inserted
Opportunity to raise an approved grade via renewed examination
No information inserted
Examiner
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 room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
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