| CYBER SECURITY (ENGLISH, THESIS) | |||||
| Master | 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 | Fall Spring |
3 | 0 | 3 | 12 |
| 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 : | Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
| Course Lecturer(s): |
Dr. Öğr. Üyesi 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. |
| 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 | |
| 1) | Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications. | |
| 1) | To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field. | |
| 1) | Being able to independently carry out a work that requires expertise in the field. | |
| 1) | To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning. | |
| 1) | To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data. | |
| 2) | To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines, | |
| 2) | To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes. | |
| 2) | To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary. | |
| 2) | To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field. | |
| 2) | To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility. | |
| 2) | To be able to comprehend the interdisciplinary interaction with which the field is related. | |
| 3) | To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies. | |
| 3) | Being able to lead in environments that require the resolution of problems related to the field. | |
| 3) | To be able to solve the problems encountered in the field by using research methods. |