| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| EEE5560 | Information Retrieval | Fall | 3 | 0 | 3 | 12 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| 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; |
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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: | 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 |
| 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 | |
| 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 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |