Nevşehir Hacı Bektaş Veli University Course Catalogue

Information Of Programmes

INSTITUTE OF SCIENCE / EEM-508 - ELECTRICAL AND ELECTRONICS ENGINEERING (MASTER)

Code: EEM-508 Course Title: APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS Theoretical+Practice: 3+0 ECTS: 6
Year/Semester of Study 1 / Spring Semester
Level of Course 2nd Cycle Degree Programme
Type of Course Optional
Department ELECTRICAL AND ELECTRONICS ENGINEERING (MASTER)
Pre-requisities and Co-requisites None
Mode of Delivery Face to Face
Teaching Period 14 Weeks
Name of Lecturer AYDIN BOYAR (aydinboyar@nevsehir.edu.tr)
Name of Lecturer(s)
Language of Instruction Turkish
Work Placement(s) None
Objectives of the Course

Learning Outcomes PO MME
The students who succeeded in this course:
LO-1 PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
PO-4 Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
PO-5 Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
PO-6 The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
PO-8 Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
Examination
LO-2 PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
PO-4 Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
PO-5 Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
PO-6 The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
PO-8 Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
Examination
LO-3 PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
PO-4 Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
PO-5 Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
PO-6 The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
PO-8 Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
Examination
LO-4 PO-1 Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
PO-2 The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
PO-4 Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
PO-5 Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
PO-6 The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
PO-8 Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
Examination
PO: Programme Outcomes
MME:Method of measurement & Evaluation

Course Contents
Weekly Course Content
Week Subject Learning Activities and Teaching Methods
1 Artificial neural networks Lecture, question and answer
2 Classification of artificial neural networks Lecture, question and answer
3 Structures of artificial neural networks Lecture, question and answer
4 Structures of artificial neural networks Lecture, question and answer
5 Learning rules of artificial neural networks Lecture, question and answer
6 Learning algorithms of artificial neural networks Lecture, question and answer
7 Learning algorithms of artificial neural networks Lecture, question and answer
8 mid-term exam
9 Artificial neural networks hardware Lecture, question and answer
10 Application areas of artificial neural networks Lecture, question and answer
11 ANN application examples in the field of engineering Lecture, question and answer
12 ANN application examples in the field of engineering Lecture, question and answer
13 ANN application examples in the field of engineering Lecture, question and answer
14 ANN application examples in the field of engineering Lecture, question and answer
15 ANN application examples in the field of engineering Lecture, question and answer
16 final exam
Recommend Course Book / Supplementary Book/Reading
1 E.Öztemel, Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul, 2003
2 Ö. Efe, O. Kaynak, Yapay Sinir Ağları ve Uygulamaları, Boğaziçi Ünv., 2000
3 Sağıroğlu, Ş., Beşdok E., Erler, M. 2003; Mühendislikte Yapay Zeka Uygulamaları - I, Ufuk Kitabevi
4 Çetin Elmas, “Yapay Sinir Ağları, Kuram, Uygulama”, Ankara: Seçkin yayınları, (2007).
5 Haykin, S., “Neural Networks- A Comprehensive Foundation”, Prentice Hall, (1999).
6 Zurada, M. J. , “Introduction to Artificial Neural Systems”, West Publishing Company, 825 p. (1992).
Required Course instruments and materials

Assessment Methods
Type of Assessment Week Hours Weight(%)
mid-term exam 8 1 40
Other assessment methods
1.Oral Examination
2.Quiz
3.Laboratory exam
4.Presentation
5.Report
6.Workshop
7.Performance Project
8.Term Paper
9.Project
final exam 16 1 60

Student Work Load
Type of Work Weekly Hours Number of Weeks Work Load
Weekly Course Hours (Theoretical+Practice) 3 14 42
Outside Class
       a) Reading 3 18 54
       b) Search in internet/Library 3 18 54
       c) Performance Project 0
       d) Prepare a workshop/Presentation/Report 0
       e) Term paper/Project 0
Oral Examination 0
Quiz 0
Laboratory exam 0
Own study for mid-term exam 1 14 14
mid-term exam 1 1 1
Own study for final exam 1 14 14
final exam 1 1 1
0
0
Total work load; 180