BÄ°L726 - INFORMATION FILTERING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| INFORMATION FILTERING | BÄ°L726 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | None. | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion | |||||
| Instructor (s) | Department Responsible (bbm-bologna@cs.hacettepe.edu.tr) | |||||
| Course objective | At the end of the course students will be able to: Discuss some of the most important problems and questions in information filtering and topic detection and tracking. Discuss the characteristics of the main components of information filtering and topic detection and tracking systems. Evaluate and propose design features to enhance information filtering and topic detection and tracking systems performance. Participate in the implementation of modern information filtering and topic detection and tracking systems. | |||||
| Learning outcomes |
| |||||
| Course Content | Conceptual framework for the design of information filtering systems. User modeling. Information filtering taxonomies and performance evaluation. Mathematical foundations. Linguistic Essentials. Corpus-based work. Text categorization. Topic detection and tracking. Probabilistic and information retrieval approaches to topic detection and tracking. | |||||
| References | Christopher D. Manning, Hinrich Schütze; Foundations of Statistical Natural Language Processing , The MIT Pres; 2002, 680 p. ISBN 0-262-13360-1 James Allan; Topic Detection and Tracking Event-based Information Organization; Kluwer Academic Publishers; 2002, 266 p. ISBN 0-7923-7664-1 | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Conceptual model of Inf. Filtering Systems |
| Week 2 | Information Filtering |
| Week 3 | Information Filtering |
| Week 4 | Semi automatic inf. filtering |
| Week 5 | Automatic inf. filtering |
| Week 6 | Midterm exam |
| Week 7 | User profiling |
| Week 8 | Inf. Filtering by using user profile |
| Week 9 | Inf. Filtering by using user profile |
| Week 10 | Collaborative filtering |
| Week 11 | Midterm exam |
| Week 12 | Spam filtering |
| Week 13 | Paper review |
| Week 14 | Paper review |
| Week 15 | Preparation to Final Exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 50 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 0 | 50 |
| Percentage of final exam contributing grade succes | 0 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 4 | 56 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 14 | 3 | 42 |
| Midterms (Study duration) | 2 | 30 | 60 |
| Final Exam (Study duration) | 1 | 40 | 40 |
| Total Workload | 45 | 80 | 240 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Graduates should have a mastery of computer science as described by the core of the Body of Knowledge. | X | ||||
| 2. Graduates need understanding of a number of recurring themes, such as abstraction, complexity, and evolutionary change, and a set of general principles, such as sharing a common resource, security, and concurrency. | X | ||||
| 3. Graduates of a computer science program need to understand how theory and practice influence each other. | X | ||||
| 4. Graduates need to think at multiple levels of detail and abstraction. | X | ||||
| 5. Students will be able to think critically, creatively and identify problems in their research. | X | ||||
| 6. Graduates should have been involved in at least one substantial project. | X | ||||
| 7. Graduates should realize that the computing field advances at a rapid pace. | X | ||||
| 8. Graduates should conduct research in an ethical and responsible manner. | X | ||||
| 9. Graduates should have good command of technical terms in both Turkish and English. | X | ||||
| 10. Graduates should understand the full range of opportunities available in computing. | X | ||||
| 11. Graduates should understand that computing interacts with many different domains. | X | ||||
| 12. Graduates should develop the knowledge acquired at master level and apply scientific methods in order to solve scientific problems. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest