COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 410ARTIFICIAL 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
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 0154455 4  54 
LO 0254455 4  54 
LO 0345455 4  54 
Sub Total1413121515 12  1512 
Contribution544550400540

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14228
Hours for off-the-classroom study (Pre-study, practice)14228
Assignments5420
Mid-terms12424
Final examination13030
Total Work Load

ECTS Credit of the Course






130

5

COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2017-2018 Spring1MERİÇ ÇETİN

Course Details
Course Code:  CENG 410 Course Title:  ARTIFICIAL NEURAL NETWORKS
L+P Hour : 3 + 0   Course Code : 1   Language Of Instruction: Turkish Course Semester :  2017-2018 Spring
Course Coordinator :  ASSOCIATE PROFESSOR MERİÇ ÇETİN E-Mail:  mcetin@pau.edu.tr Phone Number : 
Course Location MUH A0234,
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.
Attendance : %
Topics
WeeksTopics
1 Introduction, Neuron Model and Network Architectures
2 Perceptron Learning Rule
3 Signal and Weight Vector Spaces, Linear Transformations for Neural Networks
4 Supervised Hebbian Learning
5 Performance Optimization
6 Widrow-Hoff Learning
7 Backpropagation
8 Variations on Backpropagation (Drawbacks of Backpropagation, Heuristic Modifications of Backpropagation, Momentum,Conjugate Gradient,Levenberg-Marquardt Algorithm)
9 Midterm
10 Generalization
11 Dynamic Networks
12 Associative Learning (Kohonen Rule)
13 Radial Basis Networks
14 Project Demonstrations
Materials
Materials are not specified.
Resources
ResourcesResources Language
Neural Network Design, Martin T. HaganTürkçe
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam35Final Exam
Homework20Homework
Midterm Exam35Midterm Exam
Project10Project
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes
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