• 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 solutions of equations in one variable, numerical differentiation and approximating the eigenvalues of the matrices.

   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:	Review of Calculus Taylor Polynomials and Series Matlab Application
Week 2:	Solutions of Equations in One Variable The Bisection method, Fixed-Point iteration Matlab Application
Week 3:	Newton’s Method The Secant Method Matlab Application
Week 4:	The Method of False Position Error Analysis for Iterative Methods Matlab Application
Week 5:	Direct Methods for Solving Linear Systems Linear Systems of Equations Matlab Application
Week 6:	Linear Algebra and Matrix Inversion Matrix Factorization Matlab Application
Week 7:	Special Types of Matrices Matlab Application
Week 8:	Iterative Techniques in Matrix Algebra Matlab Application Midterm Exam.
Week 9:	Norms of Vectors and Matrices Eigenvalues and Eigenvectors Matlab Application
Week 10:	The Jacobi and Gauss-Siedel Iterative Techniques Matlab Application
Week 11:	Interpolation and the Lagrange Polynomial Divided Differences Method Matlab Application
Week 12:	Hermite Interpolation Matlab Application
Week 13:	Spline Interpolation Lineer and Quadratic Spline Interpolation Matlab Application
Week 14:	Cubic Spline Interpolation 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)