DD2475 Information Retrieval 9.0 credits
A course in computer science focusing on basic theory, models, and methods for information retrieval.
Educational levelSecond cycle
Academic level (A-D)C
Subject areaComputer Science and Engineering
Grade scaleA, B, C, D, E, FX, F
At present this course is not scheduled to be offered.
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 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; XML, structure of Web search engines; clustering, classification; spectral methods, random indexing; data mining.
Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology. English B, or equivalent.
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.
C. D. Manning, P. Raghavan and H. Schütze: Introduction to Information Retrieval, Cambridge University Press, 2008.
- LAB1 - Laboratory Works, 3.0 credits, grade scale: P, F
- LAB2 - Project, 3.0 credits, grade scale: A, B, C, D, E, FX, F
- TEN1 - Exam, 3.0 credits, grade 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.
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).
Hedvig Kjellström <email@example.com>
This course is replaced by DD2476 Search Engines and Information Retrieval Systems from the year 11/12.
Course plan valid from:
Examination information valid from: Autumn 10.