# GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
INTRODUCTION TO NUMERICAL ANALYSIS/ME313
 Course Title: INTRODUCTION TO NUMERICAL ANALYSIS Credits 3 ECTS 4 Course Semester 5 Type of The Course Compulsory
COURSE INFORMATION
-- (CATALOG CONTENT)
-- (TEXTBOOK)
-- (SUPPLEMENTARY TEXTBOOK)
-- (PREREQUISITES AND CO-REQUISITES)
-- LANGUAGE OF INSTRUCTION
English
-- COURSE OBJECTIVES
-- COURSE LEARNING OUTCOMES
Understanding and application of the methods to find the roots of an equations.
Students are expected to be able to apply basic numerical methods about the system of linear algebraic equations.
Students are expected to understand and apply the curve fitting methods.
Students are expected to understand and apply the basic knowledge of numerical differentiation and integration.
Students are expected to understand and apply the basic knowledge of numerical solution of differential equations.

-- MODE OF DELIVERY
The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE 1. Week INTRODUCTION: Numerical methods used for problem solving. Steps in solving a problem with a computer. Mathematical modelling. 2. Week ROOTS OF EQUATIONS: Graphical methods. Bracketing methods: Bisection and false-position methods. 3. Week ROOTS OF EQUATIONS: Open methods: Simple one-point iteration, Newton–Raphson, secant and modified Newton–Raphson methods. 4. Week SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS: Introduction. Methods for solving systems of linear algebraic equations. Gauss elimination method. 5. Week SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS: Matrix inversion method. Gauss-Seidel method. LU decomposition methods 6. Week CURVE FITTING: Introduction. Least square regression: Linear regression, polynomial regression and multiple linear regression. 7. Week CURVE FITTING: Interpolation methods: Newton Interpolation, Lagrange interpolation 8. Week CURVE FITTING: Interpolation methods: quadratic spline interpolation.Cubic spline interpolation. 9. Week NUMERICAL INTEGRATION: Introduction. Numerical integration methods: Newton–Cotes formulas, trapezoidal rule. 10. Week NUMERICAL INTEGRATION: Simpson’s rules: Simpson’s 1/3 rule, Simpson’s 3/8 rule. 11. Week NUMERICAL DIFFERENTIATION: Introduction. Finite difference approximations of the first and the second derivatives: Forward, backward and central diff 12. Week NUMERICAL DIFFERENTIATION: Derivatives of unequally spaced data. 13. Week NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS: Euler’s method, Runge–Kutta methods. 14. Week NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS 15. Week Final 16. Week Final
-- TEACHING and LEARNING METHODS
-- ASSESSMENT CRITERIA
 Quantity Total Weighting (%) Midterm Exams 2 50 Assignment 5 5 Application 0 0 Projects 0 0 Practice 2 0 Quiz 3 5 Percent of In-term Studies 60 Percentage of Final Exam to Total Score 40