ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, PHD)
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
EEE5560	Information Retrieval	Fall	3	0	3	12

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi AYÇA YALÇIN ÖZKUMUR
Recommended Optional Program Components:	none
Course Objectives:	In completing the proposed course, the students will - gain an understanding of the basic concepts and techniques in Information Retrieval; - understand how statistical models of text can be used to solve problems in IR, with a focus on how the vector-space model and the language model can be applied to the document retrieval problem; - understand how statistical models of text can be used for other IR applications, for example clustering; - appreciate the importance of data structures such as an index to allow efficient access to the information in large bodies of text; - have experience of building a document retieval system, through the practical sessions, including the implementation of a relevance feedback system; - gain an understanding of the basic operations of image processing that support IR; - understand how image processing techniques for object recognition and motion detection can be used in solving the IR problem for image data; - appreciate how combined models of language and image processing can enhance document retrieval;

Learning Outcomes

The students who have succeeded in this course;
1. Discuss the main problems of information retrieval, its uses and applications
2. Define basic steps of text representation and processing
3. Design retrieval models such as Boolean and vector space
4. Construct text indexing methods
5. Describe performance measures for search systems
6. Discuss real feedback and pseudo-relevance feedback methods
7. Describe clustering algorithms and their usage in information retrieval applications
8. Discuss page ranking methods, and the ways to improve search
9. Apply information retrieval methods to multimedia databases

Course Content

1st week: Introduction to Information Retrieval
Text representation and processing
Retrieval models
Indexing
Evaluation
Relevance feedback
Document and concept clustering
Web retrieval
Document clustering
Improving Search
Multimedia information retrieval
Automatic image annotation and retrieval
Combined models of language and image processing
14th week: Learning to rank

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to Information Retrieval
2)	Text representation and processing
3)	Retrieval models
4)	Indexing
5)	Evaluation
6)	Relevance feedback
7)	Document and concept clustering
8)	Web retrieval
9)	Document clustering
10)	Improving Search
11)	Multimedia information retrieval
12)	Automatic image annotation and retrieval
13)	Combined models of language and image processing
14)	Learning to rank

Sources

Course Notes / Textbooks:	Introduction to Information Retrieval, Christopher Manning, Prabhakar Raghavan, and Hinrich Schutze, 2008 Modern Information Retrieval (2. Eds.), Ricardo Baeza-Yates and Berthier Ribeiro-Neto, 2011
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Presentation	1	% 20
Project	1	% 40
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 20
PERCENTAGE OF FINAL WORK		% 80
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	42
Study Hours Out of Class	15	72
Presentations / Seminar	1	6
Project	11	44
Homework Assignments	6	24
Final	1	2
Total Workload		190

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

Program Outcomes

Level of Contribution