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Programme |
Graduate School of Natural and Applied Sciences Civil Engineering |
Course Information |
Course Unit Code | Course Unit Title | | Credit Pratic | Credit Lab/A | Credit Total | Credit Ects | Semester |
01INS9614 | NEURAL NETWORKS APPLICATIONS IN ENGINEERING | 3.00 | 0.00 | 0.00 | 3.00 | 6.00 | 1 |
Course Information |
Language of Instruction | Turkish |
Type of Course Unit | Elective |
Course Coordinator | Assistant Professor Dr. Kemal SAPLIOĞLU |
Course Instructors | 3-KEMAL SAPLIOĞLU |
Course Assistants | 3-KEMAL SAPLIOĞLU |
Course Aims | Increasing the use of artificial neural networks in engineering |
Course Goals | Artificial neural networks apply to engineering problems |
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 |
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. |
Prerequisities and Co-requisities Courses | |
Recommended Optional Programme Components | |
Mode Of Delivery | |
Level of Course Unit | |
Assessment Methods and Criteria | ECTS / Table Of Workload (Number of ECTS credits allocated) |
Studies During Halfterm | Number | Co-Efficient | Activity | Number | Duration | Total |
Visa | 1 | 60 | Course Duration (Excluding Exam Week) | 14 | 3 | 42 |
Quiz | 0 | 0 | Time Of Studying Out Of Class | 14 | 3 | 42 |
Homework | 14 | 20 | Homeworks | 14 | 3 | 42 |
Attendance | 1 | 10 | Presentation | 2 | 10 | 20 |
Application | 1 | 10 | Project | 0 | 0 | 0 |
Lab | 0 | 0 | Lab Study | 0 | 0 | 0 |
Project | 0 | 0 | Field Study | 0 | 0 | 0 |
Workshop | 0 | 0 | Visas | 1 | 10 | 10 |
Seminary | 0 | 0 | Finals | 1 | 10 | 10 |
Field study | 0 | 0 | Workload Hour (30) | 30 |
TOTAL | 100 | Total Work Charge / Hour | 166 |
The ratio of the term to success | 40 | Course's ECTS Credit | 6 |
The ratio of final to success | 60 | |
TOTAL | 100 | |
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) |
Work Placements |
As with any other educational component, credits for work placements are only awarded when the learning outcomes have been achieved and assessed. If a work placement is part of organised mobility (such as Farabi and Erasmus), the Learning Agreement for the placement should indicate the number of credits to be awarded if the expected learning outcomes are achieved. |
Program Learning Outcomes |
No | Course's Contribution to Program | Contribution |
1 | An ability to design, conduct laboratory experiments and analyze and interpret data, in one of the major civil engineering areas | 0 |