COMPUTER ENGINEERING (ENGLISH, NON-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 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
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 |
Recommended Optional Program Components: | None |
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 / Textbooks: | Neural Networks and Learning Machines By Simon Haykin Publisher: Prentice Hall; 3 edition |
References: | Yok - None |
Semester Requirements | Number of Activities | Level of Contribution |
Homework Assignments | 5 | % 25 |
Presentation | 1 | % 10 |
Project | 1 | % 25 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 35 | |
PERCENTAGE OF FINAL WORK | % 65 | |
Total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 14 | 42 |
Project | 13 | 65 |
Homework Assignments | 13 | 65 |
Final | 5 | 19 |
Total Workload | 191 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Define and manipulate advanced concepts of Computer Engineering | |
2) | Use math, science, and modern engineering tools to formulate and solve advenced engineering problems | |
3) | Notice, detect, formulate and solve new engineering problems. | |
4) | Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results | |
5) | Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study | |
6) | Work effectively in multi-disciplinary research teams | |
7) | Acquire scientific knowledge | |
8) | Find out new methods to improve his/her knowledge. | |
9) | Effectively express his/her research ideas and findings both orally and in writing | |
10) | Defend research outcomes at seminars and conferences. | |
11) | Prepare master thesis and articles about thesis subject clearly on the basis of published documents, thesis, etc. | |
12) | Demonstrate professional and ethical responsibility. | |
13) | Develop awareness for new professional applications and ability to interpret them. |