ELECTRICAL AND ELECTRONICS ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
EEE5560 | Information Retrieval | Fall Spring |
3 | 0 | 3 | 12 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
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; |
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 |
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 |
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 |
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: |
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 |
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 |
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. | 4 |
2) | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | 4 |
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. |