Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Faculty of Engineering and Natural Science
Programme of Computer Engineering
Course Details
Faculty of Engineering and Natural Science
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05081101 | Introduction To Neural Networks | 2025 | Spring | 8 | 3+0+0 | 3 | 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) | Asst. Prof. Neşe ÖZKAN YILMAZ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Neşe ÖZKAN YILMAZ | A BLOK-130 | [email protected] | 7812 |
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 | X |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | Adequate knowledge in mathematics, science and related engineering discipline accumulation; theoretical and practical knowledge in these areas, complex engineering the ability to use in problems. | 4 |
| P2 | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose | 5 |
| P3 | Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose | 5 |
| P4 | Ability to develop, select and use modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; ability to use information technologies effectively | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Understanding the principles of Artificial Neural Networks and learning their differences from traditional programs; | P.1.19 | 1 |
| O2 | Understanding and learning the basic structure of ANNs and various network structures; | P.2.25 | 1 |
| O3 | Learning the methods of training a simple ANN. | P.3.27 | 1,7 |
| O4 | Learning how to prepare and run an ANN project | P.4.21 | 1,7 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
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 artificial neural network |
| 11 | Simulation of ANN |
| 12 | Example YSA applications |
| 13 | Example YSA applications |
| 14 | Final examination |
Textbook or Material
| Resources | Yapay Sinir Ağları |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 4 | 56 |
| Midterms | 1 | 3 | 3 |
| 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 | 1 | 3 | 3 |
| Other | 14 | 4 | 56 |
| Total Work Load: | 160 | ||
| Total Work Load / 30 | 5,33 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P2 | P3 | P4 |
|---|---|---|---|---|---|
| O1 | Understanding the principles of Artificial Neural Networks and learning their differences from traditional programs; | 2 | - | - | - |
| O2 | Understanding and learning the basic structure of ANNs and various network structures; | - | 4 | - | - |
| O3 | Learning the methods of training a simple ANN. | - | - | 3 | - |
| O4 | Learning how to prepare and run an ANN project | - | - | - | 3 |