| Course Type | Elective Course |
|---|
| Course Level | Doctorate Of Science |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Know the theoretical and modeling issues in information retrieval.
- Know the techniques used in automatic indexing, searching and ranking output
- Know classical and user-oriented approaches used in automatic classification
- Know the roles of decision models and machine learning
- Comprehend the importance of "learning by induction"
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Theoretical and modeling issues in information retrieval: Automatic indexing. Techniques for searching and ranking output. Retrieval output evaluation. Classical and user-oriented approaches to automatic classification. Relevance feedback. Markov models. Distributed retrieval. Document filtering. Cross-language retrieval. The role of decision models and machine learning, in particular learning by observation and learning by induction, in the above processes. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Theoretical and modeling issues in information retrieval: Automatic indexing; |
|
2nd Week |
Techniques for searching and ranking output |
|
3rd Week |
Retrieval output evaluation |
|
4th Week |
Classical and user-oriented approaches to automatic classification |
|
5th Week |
Relevance feedback |
|
6th Week |
Markov models |
|
7th Week |
Markov models |
|
8th Week |
Mid-term |
|
9th Week |
Distributed retrieval |
|
10th Week |
Document filtering |
|
11th Week |
Cross-language retrieval |
|
12th Week |
Cross-language retrieval |
|
13th Week |
The role of decision models and machine learning, in particular learning by observation and learning by induction, in the above processes. |
|
14th Week |
The role of decision models and machine learning, in particular learning by observation and learning by induction, in the above processes. |
|
| Recommended or Required Reading | 1. Manning, C.D., Raghavan, P. & Schütze, H., "Introduction to Information Retrieval", Cambridge University Press, (2008).
2. Baeza-Yates, R. & Ribeiro-Neto, B., "Modern Information Retrieval: The Concepts and Technology behind Search", 2nd Edition, Addison-Wesley, (2011).
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Presentation
- Practice
- Problem Solving
- Project
- Report Preparation
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
1 |
10 |
| Project |
1 |
20 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
11 |
154 |
| Preparation for Final exam |
1 |
69 |
69 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
44 |
44 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Total Work Load | 313 |
| Total Work Load / 30 (h) | 10.43 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
