ECTS - Soft Computing
Soft Computing (CMPE466) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Soft Computing | CMPE466 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| Course Objectives | The objective of this course is to teach basic neural networks, fuzzy systems, and optimization algorithms concepts and their relations. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Biological and artificial neurons, perceptron and multilayer perceptron; ANN models and learning algorithms; fuzzy sets and fuzzy logic; basic fuzzy mathematics; fuzzy operators; fuzzy systems: fuzzifier, knowledge base, inference engine, and various inference mechanisms such as Sugeno, Mamdani, Larsen etc., composition and defuzzifier. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to Neuro – Fuzzy and Soft Computing | Chapter 1 (main text) |
| 2 | Fuzzy Sets | Chapter 2 |
| 3 | Fuzzy Rules and Fuzzy Reasoning | Chapter 3 |
| 4 | Fuzzy Rules and Fuzzy Reasoning | Chapter 3 |
| 5 | Fuzzy Inference Systems | Chapter 4 |
| 6 | Derivative – Based Optimization | Chapter 6 |
| 7 | Derivative – Free Optimization | Chapter 7 |
| 8 | Derivative – Free Optimization | Chapter 7 |
| 9 | Supervised Learning Neural Networks | Chapter 9 |
| 10 | Unsupervised Learning Neural Networks | Chapter 11 |
| 11 | Adaptive Neuro – Fuzzy Inference Systems | Chapter 12 |
| 12 | Adaptive Neuro – Fuzzy Inference Systems | Chapter 12 |
| 13 | Coactive Neuro – Fuzzy Modeling | Chapter 13 |
| 14 | Applications | Chapter 19 – 22 |
| 15 | Review | |
| 16 | Review |
Sources
| Course Book | 1. J. S. R. Jang, C. T. Sun and E. Mizutai, “Neuro-Fuzzy and Soft Computing”, 1997. |
|---|---|
| Other Sources | 2. Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1997. |
| 3. Zioluchian Ali, Jamshidi Mo, “Intelligent Control Systems Using Soft Computing Methodologies”, CRC Press, 2001. | |
| 4. D. E. Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y., 1989. | |
| 5. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003. | |
| 6. L. H. Tsoukalas, R. E. Uhrig, “Fuzzy and Neural Approaches in Engineering”, John Wiley, N. Y., 1997. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 4 | 20 |
| Presentation | - | - |
| Project | 1 | 25 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 25 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 7 | 100 |
| Percentage of Semester Work | 70 |
|---|---|
| Percentage of Final Work | 30 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems. | |||||
| 2 | Develop solutions using different technologies, software architectures and life-cycle approaches. | |||||
| 3 | An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices. | |||||
| 4 | An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements. | |||||
| 5 | Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects. | |||||
| 6 | An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain. | |||||
| 7 | An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering. | |||||
| 8 | Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies. | |||||
| 9 | An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions. | |||||
| 10 | Promote the development, adoption and sustained use of standards of excellence for software engineering practices. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 2 | 32 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 10 | 10 |
| Report | |||
| Homework Assignments | 4 | 3 | 12 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 15 | 15 |
| Total Workload | 127 | ||