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.
DD2475 Information Retrieval 9.0 credits
This course has been discontinued.
Last planned examination: Autumn 2013
Decision to discontinue this course:
No information insertedInformation 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 DD2475 (Autumn 2010–)Content and learning outcomes
Course contents
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.
Literature and preparations
Specific prerequisites
Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology. English B, or equivalent.
Equipment
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
Examination
- 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: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.
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
Opportunity to raise an approved grade via renewed examination
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
Offered by
Main field of study
Education cycle
Add-on studies
Supplementary information
This course is replaced by DD2476 Search Engines and Information Retrieval Systems from the year 11/12.