Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05081420 | Introduction To Neural Networks | 4 | 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. Ali Osman ÇIBIKDİKEN |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Ali Osman ÇIBIKDİKEN | A-124 | [email protected] | 7585 | Monday 14.00-15.00 |
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 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 | 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. | 5 | - | 3 | - |
| O4 | Learning how to prepare and run an ANN project | 1 | - | - | 3 |