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 |
|---|---|---|---|---|---|---|---|
| 05171744 | Artificial Neural Network | 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
Learning with artificial neural networks, network models, learning in artificial neural networks, current applications
Objectives of the Course
To learn the basic information about artificial neural networks and their application areas
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 | Calculation and history of artificial neural networks |
| 2 | Artificial neural networks and biological neurons, artificial neuron model |
| 3 | Activation functions, Network topologies: Feed-back and feedback networks |
| 4 | Artificial neural network models: Static and dynamic networks, decision limits |
| 5 | Training of artificial neural networks-with and without trainers |
| 6 | Sign and weight vector spaces, basic learning algorithm |
| 7 | Learning rules: Hebb rule, Perceptron rule, Delta rule, Widrow-Hoff rule, Competitive learning rule |
| 8 | Perseptron, multi-layer networks and back propagation algorithm, generalized Delta rule |
| 9 | Midterm Exam |
| 10 | CSFN Networks |
| 11 | Associated memories, Hopfield network, self-organized networks |
| 12 | Applications of artificial neural networks |
| 13 | Student Presentations |
| 14 | Student Presentations |
| 15 | Final Exam |
Textbook or Material
| Resources | S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1999 |
| S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1999 |
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 | ||