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| Course Description |
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| Course Name |
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Information Retrieval Systems |
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| Course Code |
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CENG-541 |
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| Course Type |
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Optional |
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| Level of Course |
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Second Cycle |
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| Year of Study |
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1 |
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| Course Semester |
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Fall (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
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Asst.Prof.Dr. SELMA AYŞE ÖZEL
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| Learning Outcomes of the Course |
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Knows the basic methods about information storage, processing, and retrieval.
Knows the basic data structures, inverted files and signature files used in nformation retrieval.
Obtains the basic knowledge level about information filtering, clustering based retrieval methods, search engines, and Web robots.
Knows the basic strategies that are used for information retrieval.
Learns the improvements that can be done for increasing the performance of the information retrieval systems.
Designs and implements an information retrieval system by using the basic concepts that have been learned in the class.
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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
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None |
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| Recommended Optional Programme Components |
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None |
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| Aim(s) of Course |
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To learn the basic techniques about how to store, process, and retrieve information. To obtain basic knowledge level about inverted indices, signature files, information fltering, search engines, and Web robots. |
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| Course Contents |
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Introduction to information storage and retrieval (IR). User perspective, search models, evaluation of IR systems. Formal IR models. Data structures and techniques including, inverted files, signature files, information filtering, clustering and cluster-based retrieval, hypertext and multimedia systems. IR and the Internet, browsing strategies, search engines, web robots and intelligent agents. |
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| Language of Instruction |
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English |
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| Work Place |
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Room |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Introduction to information retrieval systems and basic concepts. |
Reading the lecture notes |
Lecture, sample applications in class |
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2 |
Information retrieval strategies: vector space model, probabilistic retrieval strategy. |
Reading the lecture notes |
Lecture, sample applications in class |
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3 |
Information retrieval strategies: language models, inference networks, expanded boolean retrieval. |
Reading the lecture notes, making research for the presentation topic |
Lecture, sample applications in class |
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4 |
Information retrieval strategies: latent semantic indexing, neural networks, genetic algorithms, fuzzy set retrieval. |
Reading the lecture notes, making research for the presentation topic |
Lecture, sample applications in class |
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5 |
Performance improvement methods for the search results: relevance feedback, clustering, passage based retrieval |
Reading the lecture notes, making research for the presentation topic |
Lecture, sample applications in class |
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6 |
Performance improvement methods for the search results: N-grams, regression analysis, thesaurus |
Reading the lecture notes, preparing the oral presentation. |
Lecture, sample applications in class |
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7 |
Performance improvement methods for the search results: Semantic networks, parsing |
Reading the lecture notes, preparing the oral presentation. |
Lecture, sample applications in class |
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8 |
Performance improvement methods for the running time: Inverted Indices, Query Processing Methods |
Reading the lecture notes, implementing the Project work |
Lecture, sample applications in class |
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9 |
Performance improvement methods for the running time: Signature files, dermining the duplicates. |
Reading the lecture notes, implementing the Project work |
Lecture, sample applications in class |
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10 |
Multilanguage information retrieval, multimedia retrieval |
Reading the lecture notes, implementing the Project work |
Lecture, sample applications in class |
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11 |
Distributed information retrieval systems, parallel information retrieval systems. |
Reading the lecture notes, implementing the Project work |
Lecture, sample applications in class |
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12 |
Recent topics in information retrieval systems and student presentations. |
Reading the lecture notes, preparing the Project report |
Student oral presentations and discussion sessions. |
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13 |
Recent topics in information retrieval systems and student presentations. |
Reading the lecture notes, preparing the Project report |
Student oral presentations and discussion sessions. |
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14 |
Application of information retrieval systems and Project presentations. |
Reading the lecture notes, preparing the Project presentation |
Student oral presentations and discussion sessions. |
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15 |
Application of information retrieval systems and Project presentations. |
Reading the lecture notes, preparing the Project presentation |
Student oral presentations and discussion sessions. |
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16/17 |
Final exam. |
Reading the lecture notes |
In class written exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 D.A.Grossman, O.Frieder, "Information Retrieval Algorithms and Heuristics", second edition, Springer, 2004.
R. Baeza-Yates, B. Ribeiro-Neto, "Modern Information Retrieval", ACM press, 1999.
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| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
0 |
0 |
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Homeworks/Projects/Others |
3 |
100 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
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Final Assessments
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100 |
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Rate of Final Assessments to Success
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Reaches wide and deep knowledge through scientific research in the field of computer engineering, evaluates, implements, and comments. |
5 |
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2 |
Describes and uses information hidden in limited or missing data in the field of computer engineering by using scientific methods and integrates it with information from various disciplines. |
5 |
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3 |
Follows new and emerging applications of computer engineering profession, if necessary, examines and learns them |
5 |
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4 |
Develops methods and applies innovative approaches in order to formulate and solve problems in computer engineering. |
5 |
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5 |
Proposes new and/or original ideas and methods in the field of computer engineering in developing innovative solutions for designing systems, components or processes. |
5 |
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6 |
Designs and implements analytical modeling and experimental research and solves the complex situations encountered in this process in the field of Computer Engineering |
5 |
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7 |
works in multi disciplinary teams and takes a leading role and responsibility. |
4 |
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8 |
Learns at least one foreign language at the European Language Portfolio B2 level to communicate orally and written |
5 |
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9 |
Presents his/her research findings systematically and clearly in oral and written forms in national and international meetings. |
3 |
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10 |
Describes social and environmental implications of engineering practice. |
3 |
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11 |
Considers social, scientific and ethical values in collection, interpretation and announcement of data. |
5 |
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12 |
Acquires a comprehensive knowledge about methods and tools of computer engineering and their limitations. |
5 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
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Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
3 |
16 |
48 |
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Mid-term Exams (Written, Oral, etc.) |
0 |
0 |
0 |
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Final Exam |
1 |
20 |
20 |
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Total Workload: | 152 |
| Total Workload / 25 (h): | 6.08 |
| ECTS Credit: | 6 |
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