1. THIRD CYCLE - DOCTORATE DEGREE
  2. THE GRADUATE SCHOOL OF SOCIAL SCIENCES
  3. MANAGEMENT INFORMATION SYSTEMS DEPARTMENT
  4. 2619 MANAGEMENT INFORMATION SYSTEMS PHD
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
YOBS 639ARTIFICIAL NEURAL NETWORKS AND APPLICATIONS3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Doctorate Degree
Course Type Elective
Course Objective The aim of this course is give information about basics of artificial neural network architectures and learning rules like 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. This course aims to give the basics of artificial neural network architectures and learning rules.
Course Content The emergence of artificial neural networks(ANNs), Fundamental concepts of (ANNs), ANN structures, Learning algorithms (supervised,unsupervised), Training Single Layer ANNs, Training Multi Layer ANNs, Feed-forward neural networks, Recurrent neural networks, Deep learning
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1İlk kazanım otomatik olarak atılmaktadır. Güncellemeniz gerekmektedir. Yeni Eklemek için (+) YENİ KAZANIM EKLE tıklayınız.
2Knows the construction of single layer perceptron
3Be able to construct multi-layer perceptron
4Be able to describe the construction of different types of deep neural networks
5Be able to analyse a typical problem within the subject area and deduce which method or methods that are most suitable to solve it

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001            
LO 002            
LO 003            
LO 004            
LO 005            
Sub Total            
Contribution000000000000

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
Assignments12020
Mid-terms12929
Final examination13232
Presentation / Seminar Preparation13030
Total Work Load

ECTS Credit of the Course





195

7,5
COURSE DETAILS
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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes