GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
NUMERICAL ANALYSIS/MAT-202
Course Title: NUMERICAL ANALYSIS
Credits 3 ECTS 4
Semester 4 Compulsory/Elective Compulsory
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Assoc.Prof. Onuralp ULUER, Assist.Prof. Ali ÖZGEDİK
 -- WEB SITE(S) OF LECTURER(S)
  www.websitem.gazi.edu.tr/site/uluer, www.websitem.gazi.edu.tr/site/ozgedik
 -- EMAIL(S) OF LECTURER(S)
  uluer@gazi.edu.tr, ozgedik@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
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
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite for this course.
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  There is no recommended optional programme component for this course.
 --COURSE CONTENT
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 methods.
4. Week  ROOTS OF EQUATIONS: Secant and modified Newton–Raphson methods.
5. Week  SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS: Introduction. Methods for solving systems of linear algebraic equations. Gauss elimination method.
6. Week  SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS: Matrix inversion method. Gauss-Seidel method. LU decomposition methods
7. Week  CURVE FITTING: Introduction. Least square regression: Linear regression, polynomial regression and multiple linear regression.
8. Week  CURVE FITTING: Interpolation methods: Newton Interpolation, Lagrange interpolation, quadratic spline interpolation.Cubic spline interpolation.
9. Week  MIDTERM EXAM
10. Week  NUMERICAL INTEGRATION: Introduction. Numerical integration methods: Newton–Cotes formulas, trapezoidal rule.
11. Week  NUMERICAL INTEGRATION: Simpson’s rules: Simpson’s 1/3 rule, Simpson’s 3/8 rule.
12. Week  NUMERICAL DIFFERENTIATION: Introduction. Finite difference approximations of the first and the second derivatives: Forward, backward and central diffe
13. Week  NUMERICAL DIFFERENTIATION: Derivatives of unequally spaced data.
14. Week  NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS: Euler’s method, Runge–Kutta methods.
15. Week  NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS
16. Week  
 -- RECOMMENDED OR REQUIRED READING
  2. Gerald, C. F., Applied Numerical Analysis, Second Edition, Addison-Wesley Publishing Company, 1980. 3. Chapra, S.C., Canale, R.P., Numerical Meth
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture, Question & Answer, Drill - Practise Anlatım, Soru-Yanıt, Gösterme, Uygulama - Alıştırma
 -- WORK PLACEMENT(S)
  Not Applicable
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
40
 Assignment
0
0
 Exercises
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
14
3
42
 Practising Hours of Course Per Week
0
 Reading
10
1
10
 Searching in Internet and Library
10
2
20
 Designing and Applying Materials
0
 Preparing Reports
0
 Preparing Presentation
0
 Presentation
0
 Mid-Term and Studying for Mid-Term
1
10
10
 Final and Studying for Final
1
10
10
 Other
0
 TOTAL WORKLOAD: 
92
 TOTAL WORKLOAD / 25: 
3.68
 ECTS: 
4
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1The ability of choosing and design manufacturing systems by using contemprary methods, tools and TechnologiesX
2To be able to conduct both qualitative and quantitative scientific research methods and techniques in their major areaX
3The ability of using modern engineering methods such as computer software and contemporary methods to acquire knowledge in engineering design and analysisX
4The ability of leadership and working with multi-disciplinary projectsX
5The ability to design and conduct experiments as well as to analyze and interpret data of experimentsX
6The ability to select, develop and/or design a system, component, or process to meet desired performance, manufacturing capabilities and economic requirementsX
7Understanding of professional and ethical responsibilityX
8The communication skill of oral and written Turkish and EnglishX
9The ability of identifying, presenting, formulating, and solving manufacturing engineering problemsX
10The ability of design, execution, to analyze and evaluate of manufacturing systemsX
11The ability to apply the basic and the principles of engineering sciences for solving manufacturing problemsX
12The ability to understand and comment on the impact of manufacturing engineering solutions in a national and global context