COURSE UNIT TITLE

: TEXT BASED INFORMATION RETRIEVAL

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5061 TEXT BASED INFORMATION RETRIEVAL ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Computer Engineering Non-Thesis
COMPUTER ENGINEERING
Biomedical Tehnologies (English)
Industrial Ph.D. Program In Advanced Biomedical Technologies
Industrial Ph.D. Program In Advanced Biomedical Technologies
Computer Engineering
Computer Engineering
Computer Engineering (Non-Thesis-Evening)

Course Objective

This course introduces the basics of Text Information Retrieval and Text Mining. It focuses both theoretical aspects as well as practical uses of text mining and Information Retrieval .

Learning Outcomes of the Course Unit

1   To understand the difference between data and information retrieval
2   Learn three basic approaches of Information retrieva and text processing
3   Ability to useText mining tools
4   To evaluate a new Information retrieval and text mining approach or ability to chose right

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Information Retrieval
2 Boolean retrieval
3 The term vocabulary and postings lists
4 Index construction and compression
5 Scoring, term weighting and the vector space model
6 Computing scores in a complete search system
7 Relevance feedback and query expansion
8 XML retrieval
9 Language models for information retrieval
10 Probabilistic information retrieval
11 Student Projects
12 Student Projects
13 Student Projects
14 Student Projects

Recomended or Required Reading

Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze,An Introduction to Information Retrieval, Cambridge University Press, Cambridge, England
Sholom M. Weiss, Nitin Inurkhya, Tong Zhang, "Fundamentals of Predictive Text Mining", Springer, 2010.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 MTE MIDTERM EXAM
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE ASG * 0.30 + MTE * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.30 + MTE * 0.30 + RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Dokuz Eylül University, Department of Computer Engineering
Tınaztepe Campus 35160 Buca, Izmir
Tel:+90-(232) 301 74 01

Office Hours

Monday 9:30 - 12:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 10 3 30
Tutorials 5 2 10
Preparations before/after weekly lectures 10 2 20
Preparation for midterm exam 1 10 10
Preparing assignments 3 30 90
Preparing presentations 2 8 16
Design Project 1 20 20
Midterm 1 3 3
TOTAL WORKLOAD (hours) 199

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.15555555
LO.25555555
LO.35555555
LO.45555555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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