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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of STATISTICS and COMPUTER SCIENCES
Statistics-Joint Doctorate
Course Catalog
https://www.ktu.edu.tr/fbeistatistik
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FBE
GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of STATISTICS and COMPUTER SCIENCES / Statistics-Joint Doctorate
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ISTL7050Text-based Information Retrieval3+0+0ECTS:7.5
Year / SemesterSpring Semester
Level of CourseThird Cycle
Status Elective
DepartmentDEPARTMENT of STATISTICS and COMPUTER SCIENCES
Prerequisites and co-requisitesNone
Mode of Delivery
Contact Hours14 weeks - 3 hours of lectures per week
LecturerDr. Öğr. Üyesi Tolga BERBER
Co-Lecturer
Language of instruction
Professional practise ( internship ) None
 
The aim of the course:
Students are expected to learn and apply text mining approaches.
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : Implement text mining applications.1,3,4,51,3,4,6,
PO - 2 : Extract valuable data from document collections.1,3,4,51,3,4,6,
PO - 3 : Implementa simple search engine.1,3,4,51,3,4,6,
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome

 
Contents of the Course
Text Mining, Search Engines, Text Classification, Topic Analysis
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Introduction to information retrieval
 Week 2Text preprocessing
 Week 3Text preprocessing
 Week 4Text preprocessing
 Week 5Logical information retrieval
 Week 6Logical information retrieval
 Week 7Bag-of-words model
 Week 8TFIDF Vectorisation
 Week 9Midterm Exam
 Week 10Search engines
 Week 11Search engines
 Week 12Sınav
 Week 13Text Classification
 Week 14Text Classification
 Week 15Topic analysis
 Week 16Topic analysis
 
Textbook / Material
1Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
 
Recommended Reading
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9
15
01/01/2021
01/01/2022
1,5
1,5
50
End-of-term exam 16 10/01/2022 1,5 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 2 14 28
Arasınav için hazırlık 2 2 4
Arasınav 1.5 1 1.5
Kısa sınav 1.5 1 1.5
Total work load77