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Course Information
Course Unit Title : NEURAL NETWORKS APPLICATIONS IN ENGINEERING
Course Unit Code : 01INS9614
Type of Course Unit : Optional
Level of Course Unit : Second Cycle
Year of Study : Preb
Semester : 255.Semester
Number of ECTS Credits Allocated : 6,00
Name of Lecturer(s) : ---
Course Assistants : ---
Learning Outcomes of The Course Unit : Learning of neural networks
MATLAB-based model by the method of artificial neural networks to improve
Apply the method of artificial neural networks to engineering problems
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : Introduction to neural networks. Multi-layer artificial neural networks, training algorithms, modeling and engineering applications of the principles. Radial-based artificial neural networks, multi-layered artificial neural networks and applications according to their strengths and shortcomings. Examples of engineering application.
Languages of Instruction : Turkish
Course Goals : Artificial neural networks apply to engineering problems
Course Aims : Increasing the use of artificial neural networks in engineering
WorkPlacement  
Recommended or Required Reading
Textbook : Artificial Neural Networks Applications lecture notes (prepared by Kemal SAPLIOĞLU)
Additional Resources : Çetin Elmas (2012), Yapay Zeka Uygulamaları, Yapay Sinir Ağları ? Bulanık Mantık?Genetik Algoritma, Ankara: Seçkin Yayinevi ISBN 9789750216961

Ercan Öztemel (2006), Yapay Sinir Ağları, Istanbul: Papatya ISBN 9789756797396

Yapay Sinir Ağları İlkeleri / Zekai Şen, Su Vakfı Yayınları
Material Sharing
Documents :
Assignments :
Exams :
Additional Material :
Planned Learning Activities and Teaching Methods
Lectures, Practical Courses, Presentation, Seminar, Project, Laboratory Applications (if necessary)
ECTS / Table Of Workload (Number of ECTS credits allocated)
Student workload surveys utilized to determine ECTS credits.
Activity :
Number Duration Total  
Course Duration (Excluding Exam Week) :
14 3 42  
Time Of Studying Out Of Class :
14 3 42  
Homeworks :
14 3 42  
Presentation :
2 10 20  
Project :
0 0 0  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 10 10  
Finals :
1 10 10  
Workload Hour (30) :
30  
Total Work Charge / Hour :
166  
Course's ECTS Credit :
6      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 60
Quiz :
0 0
Homework :
14 20
Attendance :
1 10
Application :
1 10
Lab :
0 0
Project :
0 0
Workshop :
0 0
Seminary :
0 0
Field study :
0 0
   
TOTAL :
100
The ratio of the term to success :
40
The ratio of final to success :
60
TOTAL :
100
Weekly Detailed Course Content
Week Topics  
1 Introduction to neural networks
 
2 Multi-layer artificial neural networks
 
3 Multi-layer artificial neural network applications
 
4 Training algorithms
 
5 Applications training algorithms
 
6 Examples of model development
 
7 A Matlab-based applications
 
8 A Matlab-based applications and solutions
 
9 Regression analysis
 
10 Compliance test data
 
11 General applications
 
12 Evaluating the results obtained
 
13 Evaluation and application of the results obtained
 
14 General evaluation
 
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