| COMPUTER ENGINEERING (ENGLISH, INTEGRATED PHD) | |||||
| PhD | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP5133 | Artificial Neural Networks | Spring | 3 | 0 | 3 | 10 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assist. Prof. CEMAL OKAN ŞAKAR |
| Course Lecturer(s): |
Assist. Prof. CEMAL OKAN ŞAKAR |
| Course Objectives: | The objective of this course is to introduce the fundamental artificial neural network architectures and algorithms. Students will also learn to use neural networks in order to solve real world problems. |
|
The students who have succeeded in this course; I. Explain the learning and generalization aspects of neural network systems. II. Be able to apply backpropagation algorithm to a classification problem III. Be able to apply support vector machines to a classification problem. IV. Be able to implement self organizing maps. V. Describe and explain the most common architectures and learning algorithms |
| Perceptrons, linear regression, least mean squares algorithm, multi-layer perceptrons, backpropagation algorithm, support vector machines, radial basis function networks, self organizing maps, recurrent neural networks. The teaching methods of the course include lectures, individual work, technology-assisted learning, project preparation. |
| Week | Subject | Related Preparation | |
| 1) | Introduction | ||
| 2) | Perceptron | ||
| 3) | Linear regression | ||
| 4) | Least mean squares algorithm. | ||
| 5) | Multi-layer preceptrons. | ||
| 6) | Backpropagation algorithm. | ||
| 7) | Support vector machines | ||
| 8) | Support vector machines | ||
| 9) | Radial basis function networks. | ||
| 10) | Radial basis function networks | ||
| 11) | Self organizing maps | ||
| 12) | Self organizing maps | ||
| 13) | Recurrent neural networks | ||
| 14) | Recurrent neural networks | ||
| Course Notes: | Neural Networks and Learning Machines By Simon Haykin Publisher: Prentice Hall; 3 edition |
| References: | Yok - None |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | 5 | % 25 |
| Presentation | 1 | % 10 |
| Project | 1 | % 25 |
| Seminar | % 0 | |
| Midterms | % 0 | |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 35 | |
| PERCENTAGE OF FINAL WORK | % 65 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload | |
| Course Hours | 14 | 42 | |
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | |||
| Presentations / Seminar | |||
| Project | 13 | 65 | |
| Homework Assignments | 13 | 65 | |
| Quizzes | |||
| Preliminary Jury | |||
| Midterms | |||
| Paper Submission | |||
| Jury | |||
| Final | 5 | 19 | |
| Total Workload | 191 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |