GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
PATTERN RECOGNITION with ARTIFICIAL NEURAL NETWORKS/5431351
Course Title: PATTERN RECOGNITION with ARTIFICIAL NEURAL 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
  Face to face
 --WEEKLY SCHEDULE
1. Week  1. Week: Introduction to Artificial Neural Networks, Structure and Basic Elements of Artificial Neural Networks
2. Week  2. Week: First Neural Networks
3. Week  3. Week: Artificial Neural Network Model (Teacher Learning) -Multi Layer Sensor
4. Week  4. Week: Artificial Neural Network Model (Supportive Learning) -LVQ Model
5. Week  5. Week: LVQ sample application (pattern recognition)
6. Week  6. Week: Artificial Neural Network Model (Teacherless Learning)
7. Week  7. Week: Artificial Neural Network Model (Teacherless Learning)
8. Week  8. Week: Adaptive Resonance Theory (Art) Networks
9. Week  9. Week: ART Case Study (Group Technology Based Manufacturing Practice)
10. Week  10. Week: Recurrent Networks
11. Week  11. Week: ELMAN Networ
12. Week  13. Week: Applications of Artificial Neural Networks
13. Week  14. Week: Applications of Artificial Neural Networks
14. Week  15. Week: Applications of Artificial Neural Networks
15. Week  
16. Week  
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
40
 Assignment
3
20
 Application
1
20
 Projects
0
0
 Practice
1
20
 Quiz
0
0
 Percent of In-term Studies  
60
 Percentage of Final Exam to Total Score  
40
 -- 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)
   (websitem.gazi.edu.tr/site/firat)
 -- EMAIL(S) OF LECTURER(S)
   (firat@gazi.edu.tr)