DD2475 Information Retrieval 9.0 credits

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.

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.

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.

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

• 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.

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

• 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.

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