1. SECOND CYCLE - MASTER'S DEGREE
  2. THE GRADUATE SCHOOL OF SOCIAL SCIENCES
  3. MANAGEMENT INFORMATION SYSTEMS DEPARTMENT
  4. 2315 MANAGEMENT INFORMATION SYSTEMS
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
Print

COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
YOBS 543ARTIFICIAL NEURAL NETWORKS3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective The aim of the course is to give students a basic theoretical knowledge of artificial neural networks and deep learning and how to practically use them for typical problem solving processes
Course Content 1. The emergence of artificial neural networks(ANNs) 2. Fundamental concepts of (ANNs) 3. ANN structures 4. Learning algorithms (supervised,unsupervised) 5. Training Single Layer ANNs 6. Training Multi Layer ANNs 7. Feed-forward neural networks 8. Recurrent neural networks 9. Deep learning
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Can describe the ANN structures
2Knows the construction of single layer perceptron
3be able toconstruct 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
Assignments24080
Mid-terms11313
Final examination11818
Total Work Load

ECTS Credit of the Course





195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2020-2021 Fall1HÜSEYİN ÖZÇINAR


Print

Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
YOBS 543 ARTIFICIAL NEURAL NETWORKS 3 + 0 1 Turkish 2020-2021 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Assoc. Prof. Dr. HÜSEYİN ÖZÇINAR hozcinar@pau.edu.tr EGT A0305 %
Goals The aim of the course is to give students a basic theoretical knowledge of artificial neural networks and deep learning and how to practically use them for typical problem solving processes
Content 1. The emergence of artificial neural networks(ANNs) 2. Fundamental concepts of (ANNs) 3. ANN structures 4. Learning algorithms (supervised,unsupervised) 5. Training Single Layer ANNs 6. Training Multi Layer ANNs 7. Feed-forward neural networks 8. Recurrent neural networks 9. Deep learning
Topics
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes