GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
PATTERN RECOGNETION BY ARTIFICIAL NETWORKS/5851306
Course Title: PATTERN RECOGNETION BY ARTIFICIAL NETWORKS
Credits 3 ECTS 7.5
Course Semester 1 Type of The Course Elective
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
Students who succeed this course: 1. Can explain the basic concepts artificial neural networks
2. Can use artificial neural network models
3. Can develop a project on pattern recognition using artificial neural networks
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week  Introduction to Neural Networks, Artificial Neural Network Structure and Basic Elements
2. Week  First Artificial Neural Networks
3. Week  Artificial Neural Network Model (teacher learning)-Multilayer Perceptron
4. Week  Artificial Neural Network Model (Supportive Learning) - LVQ Model
5. Week  Sample Application of LVQ(Pattern Recognition)
6. Week  Artificial Neural Network Model (Unsupervised learning)
7. Week  Adaptive Resonance Theory (ART) Networks
8. Week  Sample Application of ART(Group Technology-Based Manufacturing Practice)
9. Week  Recurrent Networks
10. Week  ELMAN Networks
11. Week  Applications of Artificial Neural Networks
12. Week  Applications of Artificial Neural Networks
13. Week  Applications of Artificial Neural Networks
14. Week  Applications of Artificial Neural Networks
15. Week  
16. Week  
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
20
 Assignment
3
10
 Application
1
10
 Projects
0
0
 Practice
1
10
 Quiz
0
0
 Percent of In-term Studies  
50
 Percentage of Final Exam to Total Score  
50
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
3
42
 Weekly Tutorial Hours
0
 Reading Tasks
14
3
42
 Searching in Internet and Library
14
3
42
 Material Design and Implementation
7
3
21
 Report Preparing
6
4
24
 Preparing a Presentation
0
 Presentation
0
 Midterm Exam and Preperation for Midterm Exam
1
3
3
 Final Exam and Preperation for Final Exam
1
3
3
 Other (should be emphasized)
0
 TOTAL WORKLOAD: 
177
 TOTAL WORKLOAD / 25: 
7.08
 Course Credit (ECTS): 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1X
2X
3X
4X
5X
6X
7X
8X
9X
10X
 -- NAME OF LECTURER(S)
   (Prof. Dr. Fırat Hardalaç)
 -- WEB SITE(S) OF LECTURER(S)
   (https://websitem.gazi.edu.tr/site/firat)
 -- EMAIL(S) OF LECTURER(S)
   (firat@gazi.edu.tr)