Electrical and Electronics Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Electrical and Electronics Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Electrical and Electronics Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05171705 | Expert Systems | 4 | Autumn | 7 | 3+0+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | - |
| Instructor Assistant(s) | - |
Course Content
Definition of artificial intelligence, basic concepts and techniques, Expert Systems and engineering applications, Fuzzy logic and engineering applications, Decision support systems and applications, Genetic algorithms and application examples, Artificial neural networks: Structure and basic elements of artificial neural networks, the first artificial neural networks, artificial neural network models, feedback networks. Engineering applications of artificial neural networks
Objectives of the Course
Teaching the basic principles of artificial intelligence techniques used in engineering applications and making a detailed analysis of how they are used in applications.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction to artificial intelligence |
| 2 | Introduction of engineering applications of artificial intelligence |
| 3 | Expert Systems |
| 4 | Expert systems and engineering applications |
| 5 | Fundamentals of Fuzzy Logic |
| 6 | Fuzzy logic fundamentals and engineering applications |
| 7 | Decision support systems |
| 8 | Engineering applications of decision support systems |
| 9 | Mıd Term |
| 10 | Artificial Neural Network - Matlab |
| 11 | Artificial Neural Networks - Matlab |
| 12 | Artificial Neural Networks - Matlab |
| 13 | Project Presentations |
| 14 | Project Presentations |
| 15 | Final Exam |
Textbook or Material
| Resources | Yapay Zeka Uygulamaları, Cetin Elmas, Seçkin Yayıncılık, |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | - | - |
| Final Exam | - | - |
| Total | 0 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 0 | 0 | 0 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 0 | ||
| Total Work Load / 30 | 0 | ||
| Course ECTS Credits: | 0 | ||