GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
HEURISTIC OPTIMIZATION IN STATISTICS AND ITS APPLICATION/5551303
Course Title: HEURISTIC OPTIMIZATION IN STATISTICS AND ITS APPLICATION
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
Understand the methods of the general optimization
Understand the concept of heuristic optimization
Understand the genetic algorithm method
Understand the simulated annealing method
Understand the particle swarm optimization method
Can be perform the heuristic methods for statistical problems
 -- MODE OF DELIVERY
   The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week  Review of Linear and Nonlinear Optimization
2. Week  Linear Programming Software Applications (Linear Programming Approach for Classification Problem)
3. Week  Nonlinear Programming Software Applications (Regression Model Parameter Estimation by Search Methods)
4. Week  The concept of heuristic optimization
5. Week  Simulated Annealing
6. Week  Simulated Annealing (Maximum Likelihood Function Optimization Application)
7. Week  Genetic Algorithm - binary coding
8. Week  Genetic Algorithm - real coding, Midterm
9. Week  Genetic Algorithm (Nonlinear Regression Model Parameter Estimation Application)
10. Week  Particle Swarm Optimization
11. Week  Particle Swarm Optimization (Cluster Analysis Software Application)
12. Week  Artificial Neural Network
13. Week  Project Presentations
14. Week  Project Presentations
15. Week  Final Exam
16. Week  --
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
0
0
 Assignment
5
30
 Application
0
0
 Projects
1
20
 Practice
0
0
 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
0
 Report Preparing
10
3
30
 Preparing a Presentation
5
2
10
 Presentation
2
3
6
 Midterm Exam and Preperation for Midterm Exam
0
 Final Exam and Preperation for Final Exam
3
5
15
 Other (should be emphasized)
0
 TOTAL WORKLOAD: 
187
 TOTAL WORKLOAD / 25: 
7.48
 Course Credit (ECTS): 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
11. Based on the capabilities of undergraduate level, the students enrolled to the program can develop and deepen their knowledge and skill at the level of expertise on the same field of the undergradute study or a different field.X
22. The students use their theoretical and practical knowledge at the level of expertise in the area of statistics.X
33. The students should evaluate their acquired knowledge and skills in a critical perspective and the critical point of view guides their learning process.X
44. Theoretical and practical knowledge gained in graduate level in the field of Statistics should be applied and transfer to the current problems.X
55. By performing the process from the identification of the scientific research problem to reporting and the process should be transferred in oral, written and visual ways.X
66. The students should use computer software and information technologies on the level required by the field of Statistics.X
77. The students should have the ability to use Statistics in interdisciplinary studies.X
88. The students should have enough foreign language level to pursue statistical literature.X
99. At the required level of field of statistics, he/she should use statistical software and information technology efficiently in a such a way that helps solving problems in his/her research.X
1010. In the process of applying knowledge in a professional sense, social, scientific, and ethical values should be regarded.X
 -- NAME OF LECTURER(S)
   (Prof. H. Hasan ÖRKCÜ)
 -- WEB SITE(S) OF LECTURER(S)
   (https://abs.gazi.edu.tr/hhorkcu)
 -- EMAIL(S) OF LECTURER(S)
   (hhorkcu@gazi.edu.tr)