• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. They obtain a wide knowledge numerical integration and the numerical solutions of ordinary differential equations.

   Contribution to Program Outcomes

   1. Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
   2. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
   3. Adapt to a fast-changing technological environment, improving their knowledge and abilities constantly.
   4. Using technology as an efficient tool to understand mathematics and apply it.

   Method of assessment

   1. Written exam
2. The can use the programing language Matlab efficiently in order to compute the numerical solutions and/or approximations.

   Contribution to Program Outcomes

   1. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
   2. Adapt to a fast-changing technological environment, improving their knowledge and abilities constantly.
   3. Using technology as an efficient tool to understand mathematics and apply it.

   Method of assessment

   1. Written exam
3. They can use the theoratical results in various applications.

   Contribution to Program Outcomes

   1. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
   2. Adapt to a fast-changing technological environment, improving their knowledge and abilities constantly.
   3. Using technology as an efficient tool to understand mathematics and apply it.
   4. Exhibiting professional and ethical responsibility.

   Method of assessment

   1. Written exam

Contents
Week 1:	Numerical Differentiation
Week 2:	Numerical Differentiation
Week 3:	Numerical Differentiation - Matlab Application
Week 4:	Numerical İntegration
Week 5:	Gaussian Quadrature
Week 6:	Numerical Integration and Gaussian Quadrature - Matlab Application
Week 7:	Initial-Value Problems for Ordinary Differential Equations.
Week 8:	Euler’s Method. Midterm Exam.
Week 9:	Higher-Order Taylor Methods
Week 10:	Initial-Value Problems for ODEs -Matlab Application
Week 11:	Higher-Order Equations and Systems of Differential Equations
Week 12:	Boundary Value Problems for ODEs
Week 13:	Boundary Value Problems for ODEs
Week 14:	Higher-Order Equations and Systems of Differential Equations and Boundary Value Problems for ODEs- Matlab Application
Week 15*:	-
Week 16*:	Final Exam.
Textbooks and materials:	Richard L. Burden, John Dougles, Numerical Analysis Ward Cheney, David Kincaid, Numerical Mathematics and Computing Endre Süli, David F. Mayers, An Introduction to Numerical Analysis
Recommended readings:	Richard L. Burden, John Dougles, Numerical Analysis Ward Cheney, David Kincaid, Numerical Mathematics and Computing Endre Süli, David F. Mayers, An Introduction to Numerical Analysis
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	8	40
Other in-term studies:		0
Project:		0
Homework:		0
Quiz:		0
Final exam:	16	60
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	6	14
Practice, Recitation:	0	0
Homework:	0	0
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	10	1
Mid-term:	2	1
Personal studies for final exam:	10	1
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)