SubjectsSubjects(version: 845)
Course, academic year 2018/2019
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Information Retrieval - NPFL103
Title in English: Vyhledávání informací
Guaranteed by: Institute of Formal and Applied Linguistics (32-UFAL)
Faculty: Faculty of Mathematics and Physics
Actual: from 2017 to 2019
Semester: winter
E-Credits: 6
Hours per week, examination: winter s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: English, Czech
Teaching methods: full-time
Additional information: http://ufal.mff.cuni.cz/~pecina/ir/
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: doc. RNDr. Pavel Pecina, Ph.D.
Annotation -
Last update: T_UFAL (13.05.2014)
The course introduces modern algorithms and principles used in the field of information retrieval in large data collections. The students will gain practical knowledge and experience with experimentation and evaluation on real data. A special focus is given to web search.
Course completion requirements
Last update: doc. RNDr. Pavel Pecina, Ph.D. (12.10.2017)

Both the course credit and exam are required to complete the course.

The course credit will be given after completing two homework assignments.

The final grade will be based on the results of the exam and homework.

Literature -
Last update: T_UFAL (09.05.2012)

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. ISBN 0521865719.

Requirements to the exam
Last update: doc. RNDr. Pavel Pecina, Ph.D. (12.10.2017)

The exam is in a form of a written test with approximately 20 short-answer questions covered by the topics discussed during the course.

Syllabus -
Last update: T_UFAL (09.05.2012)

Introduction, basic concepts and principles

Boolean retrieval

Indexing

Vector space model

Evaluation in information retrieval

Query expansion

Probabilistic information retrieval

Language models for information retrieval

Text classification

Clustering

Web search

Near-duplicate detection

 
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