ELECTRICAL AND ELECTRONICS ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
EEE5560 Information Retrieval Fall
Spring
3 0 3 12
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level: Bachelor
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi AYÇA YALÇIN ÖZKUMUR
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 Outputs

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: 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
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments % 0
Presentation 1 % 20
Project 1 % 40
Seminar % 0
Midterms % 0
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
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
Laboratory
Application
Special Course Internship (Work Placement)
Field Work
Study Hours Out of Class 15 72
Presentations / Seminar 1 6
Project 11 44
Homework Assignments 6 24
Quizzes
Preliminary Jury
Midterms
Paper Submission
Jury
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
1) Adequate knowledge in mathematics, science and electric-electronic engineering subjects; ability to use theoretical and applied information in these areas to model and solve engineering problems.
2) Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.
3) Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.)
4) Ability to devise, select, and use modern techniques and tools needed for electrical-electronic engineering practice; ability to employ information technologies effectively.
5) Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems.
6) Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7) Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing.
8) Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
9) Awareness of professional and ethical responsibility.
10) Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
11) Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.