GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
HYBRID INTELLIGENT SYSTEMS/5091329
Course Title: HYBRID INTELLIGENT SYSTEMS
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
Learning artificial neural networks
Learning fuzzy systems
Learning evolutionary algorithms
Learning hybrid artificial intelligence techniques

 -- MODE OF DELIVERY
  The mode of delivery of this course is face to face
 --WEEKLY SCHEDULE
1. Week  Fuzzy systems
2. Week  Evolutionary Algorithms
3. Week  Artificial neural networks
4. Week  Artificial neural networks - fuzzy systems
5. Week  Artificial neural networks - fuzzy systems
6. Week  Artificial neural networks - fuzzy systems
7. Week  Artificial neural networks - fuzzy systems
8. Week  Artificial neural networks - evolutionary algorithms
9. Week  Artificial neural networks - evolutionary algorithms
10. Week  Artificial neural networks - evolutionary algorithms
11. Week  Artificial neural networks - fuzzy systems - evolutionary algorithms
12. Week  Artificial neural networks - fuzzy systems - evolutionary algorithms
13. Week  Artificial neural networks - fuzzy systems - evolutionary algorithms
14. Week  Hybrid systems applications
15. Week  Hybrid systems applications
16. Week  Hybrid systems applications
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
20
 Assignment
6
20
 Application
0
0
 Projects
1
20
 Practice
0
0
 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
0
0
 Reading Tasks
14
1
14
 Searching in Internet and Library
14
1
14
 Material Design and Implementation
0
0
0
 Report Preparing
8
5
40
 Preparing a Presentation
2
8
16
 Presentation
2
1
2
 Midterm Exam and Preperation for Midterm Exam
1
22
22
 Final Exam and Preperation for Final Exam
1
38
38
 Other (should be emphasized)
0
0
0
 TOTAL WORKLOAD: 
188
 TOTAL WORKLOAD / 25: 
7.52
 Course Credit (ECTS): 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1Can improve his computer engineering knowledgeX
2Can acknowledge the multidiciplinary aspects of computer engineeringX
3Can use his computer engineering knowledgeX
4Can acknowledge the multidiciplinary aspects of computer engineeringX
5Can use his computer engineering knowledge to solve problemsX
6Can define computer engineering problemsX
7Can propose solutions to computer engineering problemsX
8Can solve computer engineering problemsX
9Can evaluate solutions using qualtiy metricsX
10Can come up with new approaches to application problemsX
 -- NAME OF LECTURER(S)
   (Prof. Dr. M. Ali Akcayol)
 -- WEB SITE(S) OF LECTURER(S)
   (http://w3.gazi.edu.tr/~akcayol/)
 -- EMAIL(S) OF LECTURER(S)
   (akcayol@gazi.edu.tr)