VBM622 - SOFT COMPUTING METHODS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| SOFT COMPUTING METHODS | VBM622 | Any Semester/Year | 3 | 0 | 3 | 6 |
| Prequisites | - | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Problem Solving Other: Face to face learning, computer laboratory study, individual study | |||||
| Instructor (s) | Dr. Cagdas Hakan Aladag, Lecturer | |||||
| Course objective | To teach students to use soft computing methods effectively for real life problems. | |||||
| Learning outcomes |
| |||||
| Course Content | 1. Definition of soft computing 2. The nature of soft computing methods 3. Fields in which soft computing methods are used 4. Usage of soft computing methods in statistics 5. Soft computing approaches 6. Hybrid approaches 7. Improving soft computing approaches to solve real life problems | |||||
| References | C.H. Aladag, Introduction to integer programming, Ekin Press Ltd., ISBN 978-605-4301-79-9, 2010. (in Turkish) R.A. Aliev, Soft Computing & Its Applications, World Scientific Publishing Company, 2001. A. Celikyilmaz, I.B. Turksen, Modeling uncertainty with fuzzy logic, Springer-Verlag Berlin Heidelberg, 2009. G. Suleyman, E. Egrioglu ve C.H. Aladag, Introduction to single variable time series analysis, Hacettepe University Press, ISBN 978-975-491-242-5)ü, 2007. (in Turkish) D.K. Pratihar, Soft Computing, Alpha Science Intl Ltd, 2007. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | 1.1 What is soft computing?1.2 Why soft computing? |
| Week 2 | 1.3 Art of problem solving2.1 Basic concepts of heuristic methods |
| Week 3 | 2.2 Tabu search algorithm |
| Week 4 | 2.3 Simulated annealing |
| Week 5 | 2.4 Genetic algorithms |
| Week 6 | Midterm exam |
| Week 7 | 3.1 Artificial neural networks |
| Week 8 | 3.2 Artificial neural networks in modeling |
| Week 9 | 4.1 Fuzzy logic |
| Week 10 | 4.2 Fuzzy systems |
| Week 11 | 4.3 Methods based on fuzzy logic in statistics |
| Week 12 | Midterm exam |
| Week 13 | 5.1 Hybrid approaches |
| Week 14 | 5.2 Soft computing algorithms |
| Week 15 | 5.3 Soft computing methods to solve real life problems |
| 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 | 8 | 15 |
| Presentation | 2 | 20 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 35 |
| Final exam | 1 | 30 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 12 | 70 |
| Percentage of final exam contributing grade succes | 1 | 30 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 13 | 3 | 39 |
| Laboratory | 13 | 3 | 39 |
| Application | 10 | 2 | 20 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 12 | 6 | 72 |
| Presentation / Seminar Preparation | 2 | 10 | 20 |
| Project | 0 | 0 | 0 |
| Homework assignment | 8 | 5 | 40 |
| Midterms (Study duration) | 2 | 13 | 26 |
| Final Exam (Study duration) | 1 | 14 | 14 |
| Total Workload | 61 | 56 | 270 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Has detailed knowledge about data and knowledge engineering (DKE). | X | ||||
| 2. Has a good understanding of common concepts such as abstraction, complexity, security, concurrency, software lifecycle and applies their expertise to the effective design, development and management of IS. | X | ||||
| 3. Understands the interaction of theory and practice and the links between them. | X | ||||
| 4. Has the ability to think at different levels of abstraction and detail; understands that an IS can be considered in different contexts, going beyond narrowly identifying implementation issues. | X | ||||
| 5. Solves any technical or scientific problem independently and presents the best possible solution; has the communication skills to clearly explain the completeness and assumptions of their solution. | X | ||||
| 6. Completes a project on a larger scale than an ordinary course project in order to acquire the skills necessary to work efficiently in a team. | X | ||||
| 7. Recognises that the field of DKE is rapidly evolving. Follows the latest developments, learns and develops skills throughout their career. | X | ||||
| 8. Recognises the social, legal, ethical and cultural issues related to DKE practice and conduct professional activities in accordance with these issues. | X | ||||
| 9. Can make oral presentations in English and Turkish to different audiences face-to-face, in writing or electronically. | X | ||||
| 10. Recognises that DKE has a wide range of applications and opportunities. | X | ||||
| 11. Is aware that DKE interacts with different fields, can communicate with experts from different fields and can learn necessary field knowledge from them. | X | ||||
| 12. Define a research problem and use scientific methods to solve it. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest