Electrical and Computer Engineering Graduate With Thesis
Course Details
KTO KARATAY UNIVERSITY
Graduate Education Institute
Programme of Electrical and Computer Engineering Graduate With Thesis
Course Details
Graduate Education Institute
Programme of Electrical and Computer Engineering Graduate With Thesis
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 80511109 | Artificial Neural Network | 2023 | Autumn | 1 | 3+0+0 | 7,5 | 7,5 |
| Course Type | Elective |
| Course Cycle | Master's (Second Cycle) (TQF-HE: Level 7 / QF-EHEA: Level 2 / EQF-LLL: Level 7) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | - |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Novruz Allahverdi | - |
Course Content
A neural model. The comparisonof a traditional computer artificial neural networks (ANN). ANN learning problems. Multilayer Neural Networks. counterpropagation algorithm Back propagation algorithm. Two-way assosiatif memory systems. Hoppfield ANN's. Examples of applications ANN's in the industry, medicine and other fields. A simple design of the project on the subject.
Objectives of the Course
Learn how to design an ANN.
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 | Examination of how a human nerve works |
| 2 | Various neural models (electronic, larynx, mathematical) |
| 3 | Examination of Artificial Neural Network (YSA) models |
| 4 | Classification of various YSAs |
| 5 | Single and multi-level YSA models |
| 6 | Backpropagation algorithm |
| 7 | YSA Training methods |
| 8 | Counter propagation algorithm, other algorithms |
| 9 | Midterm |
| 10 | Hoppfield YSA. A simple YSA design on the subject. Homework. |
| 11 | Example YSA applications. Homework check |
| 12 | Example YSA applications. Homework check |
| 13 | Example YSA applications. Homework check |
| 14 | Ödev sunumu ve Final sınavı |
Textbook or Material
| Resources | Novruz Allahverdi, Artificial Neural Networks, Lecture notes, http://farabi.suzep.gen.tr/ |
| Novruz Allahverdi, Artificial Neural Networks, Lecture notes, http://farabi.suzep.gen.tr/ |
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 | ||