COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
EEEN 479OPTIMIZATION TECHNIQUES3 + 07th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective To teach gradient-based unconstrained numerical optimization techniques. To implement these techniques in MatLab environment. To use these techniques in solving real-world problems.
Course Content One-dimensional nonlinear numerical optimization / Multi-dimensional nonlinear numerical optimization / Mathematical background / Analytical conditions for optimality / First-order methods / Second-order methods / Second-order approximate methods / Applications
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to face

COURSE LEARNING OUTCOMES
1He/She knows the fundamental concepts of numerical optimization.
2He/She knows gradient-based unconstrained numerical optimization methods.
3He/She can solve related real-world problems by optimization methods.
4He/She can do modeling and prediction by Artificial Neural Networks.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 01 443        
LO 02 555        
LO 034544        
LO 045554        
Sub Total9191816        
Contribution255400000000

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)14570
Mid-terms188
Final examination11010
Total Work Load

ECTS Credit of the Course






130

5

COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2015-2016 Fall3MERİÇ ÇETİN

Course Details
Course Code:  EEEN 479 Course Title:  OPTIMIZATION TECHNIQUES
L+P Hour : 3 + 0   Course Code : 3   Language Of Instruction: Turkish Course Semester :  2015-2016 Fall
Course Coordinator :  ASSOCIATE PROFESSOR MERİÇ ÇETİN E-Mail:  mcetin@pau.edu.tr Phone Number : 
Course Location MUH A02151,
Goals : To teach gradient-based unconstrained numerical optimization techniques. To implement these techniques in MatLab environment. To use these techniques in solving real-world problems.
Content : One-dimensional nonlinear numerical optimization / Multi-dimensional nonlinear numerical optimization / Mathematical background / Analytical conditions for optimality / First-order methods / Second-order methods / Second-order approximate methods / Applications
Attendance : %
Topics
WeeksTopics
1 Introduction to optimization, optimization problem
2 Unconstrained optimization
3 Numeric Optimization
4 Indirect Methods: Newton Raphson, Bisection methods and Matlab applications
5 Direct Methods: Golden Section and Matlab applications
6 Algorithms for unconstrained multivariable optimization
7 Gradient methods
8 Steepest Descent, Conjugate Gradient methods
9 Midterm
10 Newton methods, Quasi-Newton methods
11 Non-Gradient Methods, Regression
12 Linear and Nonlinear Models
13 SISO Neural Network Model and Matlab applications
14 MIMO Neural Network Model and Matlab applications
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam40Final Exam
Midterm Exam40Midterm Exam
Homework20Homework
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes
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