ARTIFICIAL INTELLIGENCE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
MATH3012	Numerical Analysis	Spring	2	2	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :
Recommended Optional Program Components:	None
Course Objectives:	Numerical Analysis is concerned with the mathematical derivation, description and analysis of obtaining numerical solutions of mathematical problems. We have several objectives for the students. Students should obtain an intuitive and working understanding of some numerical methods for the basic problems of numerical analysis. They should gain some appreciation of the concept of error and of the need to analyze and predict it. And also they should develop some experience in the implementation of numerical methods by using a computer.

Learning Outcomes

The students who have succeeded in this course;

The students who succeeded in this course;
o will be able to define Errors, Big O Notation, Stability and Condition Number, Taylor’s Theorem.
o will be able to solve Nonlinear Equations.
o will be able to solve Linear Systems.
o will be able to use Iterative Methods for Linear Systems.
o will be able to calculate Eigenvalues and Eigenvectors.
o will be able to solve System of Nonlinear Equations.
o will be able to calculate Interpolating and Polynomial Approximation.

Course Content

In this course the solution of linear and nonlinear systems will be discussed numerically. Also iterative methods for linear systems will be taught.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Errors, Big O Notation, Stability and Condition Number, Taylor’s Theorem.
2)	The Solution of Nonlinear Equations in the form of f(x)=0: Bisection Method, Fixed Point Iteration.
3)	Newton-Rapson Method, Secant Method.
4)	The Solution of Linear System : Solving Triangular System, Gauss Elimination and Pivoting.
5)	LU Factorization, Tridiagonal System, Vector and Matrix Norms
6)	Sensitivity of Linear Equations. Condition Number and Stability.
7)	Iterative Methods for Linear Systems: Jacobi Method.
8)	Gauss Seidel Method. Diagonally Dominant Matrix. Errors in Solving Linear Systems.
9)	Eigenvalues and Eigenvectors: The Power Method. The Inverse Power Method.
10)	System of Nonlinear Equations: Newton’s Method.
11)	Interpolating and Polynomial Approximation: Lagrange interpolation polynomial, Newton Interpolation.
12)	Piecewise Linear Interpolation, Cubic Spline.
13)	Least Square Approximation: Curve Fitting.
14)	Inconsistent System of Equations. Errors in Interpolation .

Sources

Course Notes / Textbooks:	Numerical Methods Using MATLAB (Fourth Edition), John H. Mathews and Kurtis D. Fink, Pearson Prentice Hall
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance	16	% 0
Laboratory	16	% 5
Quizzes	5	% 10
Midterms	2	% 45
Final	1	% 45
Total		% 105
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 45
Total		% 105

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	16	3	48
Laboratory	14	1	14
Study Hours Out of Class	16	2	32
Quizzes	3	5	15
Midterms	2	5	10
Final	1	5	5
Total Workload			124

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Have sufficient background in mathematics, science and artificial intelligence engineering.
2)	Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions.
3)	Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose.
4)	Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction.
5)	Select and use modern techniques and tools necessary for engineering applications.
6)	Design and conduct experiments, collect data, and analyse and interpret results.
7)	Work effectively both as an individual and as a multi-disciplinary team member.
8)	Access information via conducting literature research, using databases and other resources
9)	Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning.
10)	Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field.
11)	Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level.
12)	Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age.
13)	Have a sense of professional and ethical responsibility.
14)	Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices.