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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
YBS 456ARTIFICIAL NEURAL NETWORKS3 + 08th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective This course aims to give the basics of artificial neural network architectures and learning rules.
Course Content Definition of artificial neural networks (ANN) ADALINE: adaptive linear element, Learning: supervised and unsupervised learning Linear Associative Memory, Multi-layer perceptron Back-propagation method, Radial-basis ANN Dynamic ANN, Hopfield Network Cellular ANN.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Learning articial neural networks
2Understanding the application areas
3Enhancing the subject area by a sample project on an application study

COURSE'S CONTRIBUTION TO PROGRAM
Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14342
Assignments5525
Mid-terms11010
Final examination11111
Total Work Load

ECTS Credit of the Course






130

5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2023-2024 Spring2HAMİD YEŞİLYAYLA


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
YBS 456 ARTIFICIAL NEURAL NETWORKS 3 + 0 2 Turkish 2023-2024 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Lecturer HAMİD YEŞİLYAYLA hyesilyayla@pau.edu.tr İİBF B0213 %70
Goals This course aims to give the basics of artificial neural network architectures and learning rules.
Content Definition of artificial neural networks (ANN) ADALINE: adaptive linear element, Learning: supervised and unsupervised learning Linear Associative Memory, Multi-layer perceptron Back-propagation method, Radial-basis ANN Dynamic ANN, Hopfield Network Cellular ANN.
Topics
WeeksTopics
1 Characteristic Features of ANN
2 Neuron Model
3 Supervised Learning
4 Unsupervised Learning
5 Network Architectures and Perceptron Model
6 Multilayer Deed-Forward Networks
7 Back-Propagation
8 Attributed Memory Feature
9 Self organizing maps and Adaptive resonance theory
10 Other ANN architectures
11 Some Classical NN Architectures and Applications
12 Implementation and Applications of ANN
13 Presentations
14 Presentations
Materials
Materials are not specified.
Resources
ResourcesResources Language
Neural Networks Toolbox 6 User GuideTürkçe
M. T. Hagan, H. B. Demuth, M. H. Beale, I. O. De Jesús, "Neural Network Design 2 Edition eBook "Türkçe
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam60Final Exam
Midterm Exam40Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes