Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
8BLMS414Artificial Neural Networks3+0+03520.02.2026

 
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Computer Engineering
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The aim of this course is to introduce the basic principles and techniques of ANNs, to examine basic ANN models and to teach their applications.
Course Content The course content can be listed as: Introduction, Threshold Gates, computational ability of ANNs, Learning Rules, Mathematical Theory of Neural Learning, Adaptive Multilayer ANNs, Adaptive Multilayer ANNs, Associated Neural Network Memories, Universal Scanning Methods in ANNs and Self Organized Systems.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator Dr. Arda AKDEMİR
Name of Lecturers Dr. ARDA AKDEMİR
Assistants Research Assist. Sinem MİZANALI
Work Placement(s) No

Recommended or Required Reading
Resources Fundamentals of Artificial Neural Networks, M. Hassoun, 1995, MIT Press.
Neural Networks, S. Haykin, Mc Millian Book Co., 1994

Course Category
Mathematics and Basic Sciences %30
Engineering %30
Engineering Design %20
Field %20

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
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 1 14
Mid-terms 1 2 2
Final examination 1 2 2
Total Work Load   Number of ECTS Credits 2 60

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Upon successful completion of this module, students can describe the relationship between simple ANN models and a real brain.
2 They can distinguish between similarities and differences between Kohonen-type self-organizing networks. In addition, they have information about the performance factors that affect learning in ANNs.
3 Gain knowledge of real classification and regression applications of ANNs.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Entrance, Threshold Gates
2 The computational capability of ANNs
3 Learning Rules
4 Mathematical Theory of Neural Learning
5 Adaptive Multilayer ANN
6 Applications
7 Adaptive Multilayer ANN II
8 Midterm
9 Associated Neural Network Memories
10 Universal Scanning Methods in ANNs
11 Self Organized Systems
12 Self Organized Systems
13 Self Organized Systems
14 Final Exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 2 3 3
C1
C2
C3

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  https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=243263&curProgID=5607&lang=en