Information Package / Course Catalogue - Adnan Menderes University

General Information Unit Staff Academic Information Programme Outcomes Course Structure Diagram Contact Information

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Numerical Analysis

Course Code	: CSE317
Course Type	: Required
Couse Group	: First Cycle (Bachelor's Degree)
Education Language	: English
Work Placement	: N/A

Theory	: 2
Prt.	: 2
Credit	: 3
Lab	: 0
ECTS	: 5

Objectives of the Course

The main objective of this course is to introduce the language, logic, and math of numerical methods as used in computer engineering and in computer science. Another objective is to understand the various types of numerical methods so that we know capabilities and limitations in different domains.

Course Content

To explain error measurement techniques, linear equations, iterative solutions to non-linear equations, interpolation and other numerical analysis methods theoretically. To implement mentioned topics with the help of Python programming language.

Name of Lecturer(s)

Prof. İnci ERHAN

Learning Outcomes

1.	To identify complex mathematical problems
2.	To create solutions for both linear and non-linear problems
3.	To use iterative approaches to analyze problems
4.	To produce proper algorithms to solve complex problems
5.	To apply numerical methods to real world engineering applications

Recommended or Required Reading

1.	Jaan Kiusalaas; “Numerical Methods in Engineering with Python 3”, 3rd Ed., Cambridge University Press, 2013.
2.	Timothy Sauer, “Numerical Analysis”, 2nd Ed., Pearson, 2011.
3.	Ward Cheney and David Kincaid, “Numerical Mathematics and Computing”, 7th Ed., Brooks Cole, 2012.

Weekly Detailed Course Contents

Week 1 - Theoretical

Introduction, preliminaries

Week 2 - Theoretical

Introduction to Python

Week 3 - Theoretical

Mathematics Modules in Python

Week 4 - Theoretical

Measuring Errors

Week 5 - Theoretical

Systems of Linear Algebraic Equations

Week 6 - Theoretical

Matrix Inversion&Iterative Methods

Week 7 - Theoretical

Polynomial Interpolation

Week 8 - Theoretical

Least squares approximations

Week 9 - Theoretical

Roots of Equations

Week 10 - Theoretical

Numerical Differentiation

Week 11 - Theoretical

Numerical Integration

Week 12 - Theoretical

Romberg&Gaussian Integration

Week 13 - Theoretical

Initial Value & Two Point Boundary Value Problems

Week 14 - Theoretical

Symmetric Matrix Eigenvalue Problems

Assessment Methods and Criteria

Type of Assessment	Count	Percent
Midterm Examination	1	%30
Final Examination	1	%60
Assignment	2	%10

Workload Calculation

Activities	Count	Preparation	Time	Total Work Load (hours)
Lecture - Theory	14	1	2	42
Lecture - Practice	14	1	2	42
Assignment	2	6	0	12
Midterm Examination	1	10	2	12
Final Examination	1	15	2	17
TOTAL WORKLOAD (hours)				125

Contribution of Learning Outcomes to Programme Outcomes

	PÇ-1	PÇ-2	PÇ-3	PÇ-4	PÇ-5	PÇ-6	PÇ-7	PÇ-8	PÇ-9	PÇ-10	PÇ-11
OÇ-1	5	4						3
OÇ-2	5	5	4	5	4	4		4
OÇ-3	4	5	4	3	4	4		4
OÇ-4	4	5	4	5	4			4
OÇ-5	4	5		4

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