|
Description of course unit
|
|
Prerequisites and course requisities
|
-
|
|
Language of instruction
|
Turkish
|
|
Coordinator
|
Assoc.Prof.Dr.İlke TÜRKMEN
|
|
Lecturer(s)
|
Assoc.Prof.Dr.İlke TÜRKMEN
|
|
Teaching assitant(s)
|
-
|
|
Mode of delivery
|
Face-to-face training is provided. This course is arranged to be taught 3 hours of theorical lectures per week.
|
|
Course objective
|
Provide the acquisition of artificial neural networks and to solve different aviation problems using artificial neural networks.
|
|
Course description
|
This course covers the artificial neural networks basic knowledges and their applications to aviation problems.
|
|
1- |
Introduction to ANN, ANN.
|
|
2- |
Perceptron, Hebbian learning, gradient descent learning, delta rule.
|
|
3- |
Multi layer Perceptron
|
|
4- |
Bias and variability,
|
|
5- |
Applications of multi layer perceptron
|
|
6- |
Applications of multi layer perceptron
|
|
7- |
Applications of multi layer perceptron
|
|
8- |
Radial Basis Functions Networks
|
|
9- |
SOM
|
|
10- |
Applications of ANN to different aviation problems
|
|
11- |
Applications of ANN to different aviation problems
|
|
12- |
Applications of ANN to different aviation problems
|
|
13- |
Applications of ANN to different aviation problems
|
|
14- |
-
|
|
15- |
|
|
16- |
|
|
17- |
|
|
18- |
|
|
19- |
|
|
20- |
|
|
Learning outcomes of the course unit
|
|
1- |
At the end of this course students will obtain basic information about the ANN.
|
|
2- |
-
|
|
3- |
-
|
|
4- |
-
|
|
5- |
-
|
|
6- |
-
|
|
7- |
|
|
8- |
|
|
9- |
|
|
10- |
|
|
*Contribution level of the course unit to the key learning outcomes
|
|
1- |
|
|
|
2- |
|
|
|
3- |
|
|
|
4- |
|
|
|
5- |
|
|
|
6- |
|
|
|
7- |
|
|
|
8- |
|
|
|
9- |
|
|
|
10- |
|
|
|
11- |
|
|
|
12- |
|
|
|
13- |
|
|
|
14- |
|
|
|
15- |
|
|
|
16- |
|
|
|
17- |
|
|
|
18- |
|
|
|
19- |
|
|
|
20- |
|
|
|
21- |
|
|
|
22- |
|
|
|
23- |
|
|
|
24- |
|
|
|
25- |
|
|
|
26- |
|
|
|
27- |
|
|
|
28- |
|
|
|
29- |
|
|
|
30- |
|
|
|
31- |
|
|
|
32- |
|
|
|
33- |
|
|
|
34- |
|
|
|
35- |
|
|
|
36- |
|
|
|
37- |
|
|
|
38- |
|
|
|
39- |
|
|
|
40- |
|
|
|
41- |
|
|
|
42- |
|
|
|
43- |
|
|
|
44- |
|
|
|
45- |
|
|
|
Number of stars refer to level of contribution from 1 (the least) to 5 (the most) |
|
Planned learning activities, teaching methods and ECTS work load
|
|
|
Quantity
|
Time (hour)
|
Quantity*Time (hour)
|
|
Lectures (face to face teaching)
|
14
|
3
|
42
|
|
Study hours out of classroom (study before and after the class)
|
10
|
5
|
50
|
|
Homework
|
3
|
5
|
15
|
|
Presentation / seminar
|
2
|
5
|
10
|
|
Quiz
|
0
|
0
|
0
|
|
Preparation for midterm exams
|
1
|
10
|
10
|
|
Midterm exams
|
1
|
2
|
2
|
|
Project (term paper)
|
2
|
10
|
20
|
|
Laboratuar
|
0
|
0
|
0
|
|
Field study
|
0
|
0
|
0
|
|
Preparation for final exam
|
1
|
10
|
10
|
|
Final exam
|
1
|
2
|
2
|
|
Research
|
5
|
5
|
25
|
|
Total work load
|
|
|
186
|
|
ECTS
|
|
|
7.50
|
|
Assessment methods and criteria
|
|
Evaluation during semester
|
Quantity
|
Percentage
|
|
Midterm exam
|
1
|
100
|
|
Quiz
|
0
|
0
|
|
Homework
|
0
|
0
|
|
Semester total
|
|
100
|
|
Contribution ratio of evaluation during semester to success
|
|
40
|
|
Contribution ratio of final exam to success
|
|
60
|
|
General total
|
|
100
|
|
Recommended and required reading
|
|
Textbook
|
1. S. Haykin, Neural Networks and Learning Machines, Pearson Education, 3rd Ed., 2009, ISBN13 9780131293762 ISBN10 0131293761 2. J. M. Zurada, Int. To Artificial Neural Systems, West Publishing Company, 1992 ISBN 053495460X, 9780534954604.
|
|
Additional references
|
-
|
|
Files related to the course unit
|
|
|
|