| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Understand scientifıc and mathematical principles and apply them to the practice of engineering.
- Gain an ability to use Computer programs as tools for system design and analysis applications.
- Have an ability to apply the advanced principles of measurement. data analysis, and design of experiments.
- Have an ability to apply the system/process design levels to industrial engineering problems, including the consideration of different technical alternatives while bearing in mind cost, environmental concems, safety, and other constraints.
- Have an ability to analyze, measure, test, and evaluate an industrial engineering problem.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Theoretical foundations of artificial neural networks, tools of artificial neural networks, learning algorithms in artificial neural networks and architecture, modeling of artificial neural networks, verification analysis, forecasting with artificial neural networks, applications in Industrial Engineering. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
The basic structure of artificial neural Networks, advantages and disadvantages |
|
2nd Week |
Similarity between human brain and neural networks |
|
3rd Week |
Neural Netvvork Pattems: Biological neurons, Artificial nerve celi |
|
4th Week |
Neural Network Patterns : Basic components |
|
5th Week |
Leaming in Artificial Neural Networks: Basic learning rules |
|
6th Week |
Leaming in Artificial Neural Networks: Basic learning rules |
|
7th Week |
ANN Architectures |
|
8th Week |
ANN Modelling |
|
9th Week |
Midterm |
|
10th Week |
ANN Verification Analysis |
|
11th Week |
Industrial Engineering Applications with ANN |
|
12th Week |
Industrial Engineering Applications with ANN |
|
13th Week |
Forecasting with ANN |
|
14th Week |
Forecasting Applications with ANN |
|
| Recommended or Required Reading | Fausett, L., Fundementals ofNeural Networks, 1994.
Sağıroğlu, Ş., Beşdok, E., Erler, M., Mühendislikte Yapay Zeka Uygulamaları-1 Yapay Sinir Ağları, Ufuk Yayıncılık, 2003.
|
|---|
| Planned Learning Activities and Teaching Methods |
- Presentation
- Other
- Lecture
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
25 |
| Assignment |
1 |
10 |
| Quiz |
1 |
15 |
| Practice |
1 |
15 |
| Total(%) |
|
65 |
| Contribution of In-term Studies to Overall Grade(%) |
|
65 |
| Contribution of Final Examination to Overall Grade(%) |
|
35 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
1 |
4 |
4 |
| Individual or group work |
14 |
2 |
28 |
| Preparation for Final exam |
1 |
25 |
25 |
| Course hours |
14 |
4 |
56 |
| Preparation for Midterm exam |
1 |
25 |
25 |
| Laboratory (including preparation) |
1 |
2 |
2 |
| Final exam |
1 |
2 |
2 |
| Homework |
1 |
8 |
8 |
| Total Work Load | 152 |
| Total Work Load / 30 (h) | 5.06 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
