INFORMATION TECHNOLOGIES (ENGLISH, NONTHESIS) | |||||
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 | 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 : | Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
Course Lecturer(s): |
Dr. Öğr. Üyesi 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. |
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 |