Syllabus ( MATH 310 )

Basic information


Course title: 
Numerical Analysis I 
Course code: 
MATH 310 
Lecturer: 
Assoc. Prof. Dr. Hülya ÖZTÜRK

ECTS credits: 
6 
GTU credits: 
4 (3+2+0) 
Year, Semester: 
3, Fall 
Level of course: 
First Cycle (Undergraduate) 
Type of course: 
Compulsory

Language of instruction: 
English

Mode of delivery: 
Face to face

Pre and corequisites: 
yok 
Professional practice: 
No 
Purpose of the course: 
Students who can successfully complete this course are equipped with the skills to analyze various aspects, such as the solutions of equations in one variable,numerical differentiation, linear algebra and to draw appropriate conclusions in practical applications. 



Learning outcomes


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

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

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.

Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.

Adapt to a fastchanging technological environment, improving their knowledge and abilities constantly.

Using technology as an efficient tool to understand mathematics and apply it.
Method of assessment

Written exam

The can use the programing language Matlab efficiently in order to compute the numerical solutions and/or approximations.
Contribution to Program Outcomes

Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.

Adapt to a fastchanging technological environment, improving their knowledge and abilities constantly.

Using technology as an efficient tool to understand mathematics and apply it.
Method of assessment

Written exam

Homework assignment

They can use the theoratical results in various applications.
Contribution to Program Outcomes

Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.

Adapt to a fastchanging technological environment, improving their knowledge and abilities constantly.

Using technology as an efficient tool to understand mathematics and apply it.

Exhibiting professional and ethical responsibility.
Method of assessment

Homework assignment


Contents


Week 1: 
Review of Calculus Taylor Polynomials and Series Matlab Application 
Week 2: 
Solutions of Equations in One Variable The Bisection method, FixedPoint 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 Iterative Techniques in Matrix Algebra Matlab Application

Week 8: 
Midterm exam and solutions 
Week 9: 
Norms of Vectors and Matrices Eigenvalues and Eigenvectors Matlab Application

Week 10: 
The Jacobi and GaussSiedel 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 (%) 

Midterms: 
8 
40 
Other interm 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 (Facetoface 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 midterm exam: 
10 
1 

Midterm: 
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)



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