| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits | Last Updated Date |
| 1 | EEE506 | AN INTRODUCTION TO NEURAL NETWORKS | 3+0+0 | 3 | 6 | 16.06.2026 |
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Language of Instruction
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English
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Level of Course Unit
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Master's Degree
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Department / Program
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ELECTRICAL AND ELECTRONICS ENGINEERING
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Type of Program
|
Formal Education
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Type of Course Unit
|
Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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Introducing the main fundamental principles and techniques of neural network systems. Investigating the principal neural network models and applications.
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Course Content
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Neuron models and network architectures. Single-layer neural networks. Perceptron learning rule. Weight Vector Spaces. Linear transformations for neural networks. Hebb learning rule. Performance surfaces. Performance learning and optimization. Widrow-Hoff learning. Multilayer neural networks. Backpropagation algorithm. Neural networks applications: classification and regression.
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Course Methods and Techniques
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|
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Prerequisites and co-requisities
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None
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Course Coordinator
|
None
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Name of Lecturers
|
Associate Prof.Dr. Serkan ÖZBAY
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
|
Fundamentals of Neural Networks Architectures, Algorithms, and Applications Laurene Fausett
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Course Notes
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Neural Network Design (Second Edition)
Martin T. Hagan
Howard B. Demuth
Mark Hudson Beale
Orlando De Jesús
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Course Category
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Mathematics and Basic Sciences
|
%30
|
|
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Engineering
|
%30
|
|
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Engineering Design
|
%40
|
|
|
Social Sciences
|
%0
|
|
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Education
|
%0
|
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Science
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%0
|
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Health
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%0
|
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Field
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%0
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Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
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In-Term Studies
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Mid-terms
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2
|
%
40
|
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Project
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1
|
%
20
|
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Final examination
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1
|
%
40
|
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Total
|
4
|
%
100
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ECTS Allocated Based on Student Workload
|
Activities
|
Total Work Load
|
|
Weekly lecture hours
|
14
|
3
|
42
|
|
Reading Activities
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14
|
3
|
42
|
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Internet browsing, library work
|
1
|
4
|
4
|
|
Material design, application
|
1
|
8
|
8
|
|
Report preparation
|
1
|
8
|
8
|
|
Presentation preparation
|
1
|
8
|
8
|
|
Presentation
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1
|
8
|
8
|
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Midterm and midterm exam preparation
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2
|
20
|
40
|
|
Final exam and preparation for the final exam
|
1
|
20
|
20
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Total Work Load
| |
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Number of ECTS Credits 6
180
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Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
|
1
| Describing the relation between real brains and simple artificial neural network models |
|
2
| Explaining the most common architectures and learning algorithms in neural networks |
|
3
| Discussing the main factors involved in achieving good learning and generalization performance in neural network systems |
|
4
| Evaluating the practical considerations in neural network applications |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Principals of neural computing
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| 2 |
Neuron model and network architectures
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| 3 |
Neuron model and network architectures
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| 4 |
Architectural analysis of different neural network models: Feedforward (Perceptron) model
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| 5 |
Recurrent (Hopfield) model, Competitive (Hamming) model
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| 6 |
Learning algorithms:Supervised and unsupervised learning
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| 7 |
Hebb learning rule
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| 8 |
Perceptron learning rule
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| 9 |
LMS algorithm
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| 10 |
Performance learning
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| 11 |
Backpropagation algorithm
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| 12 |
Backpropagation algorithm
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| 13 |
Dynamics of recurrent neural networks
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| 14 |
Dynamics of recurrent neural networks
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Contribution of Learning Outcomes to Programme Outcomes
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https://obs.gantep.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=147925&lang=en