ELE375

Mathematical Methods for Electrical Engineers

Faculty \ Department
School of Engineering \ Electrical and Electronics Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
3
6
Compulsory
Turkish
Prerequisites
MAT 201, MAT 202
Programs that can take the course
Electrical and Electronics Engineering
Course Description
Learning Numerical Methods to Solve Engineering Problems
Textbook and / or References
Numerical Methods in Engineering with MATLAB, Jaan Kiusalaas, 2. Basım, Cambridge University Pres, 2009.
Course Objectives
Learning numerical methods and methods used in solving different engineering problems that are frequently encountered in Electrical and Electronics Engineering.
Course Outcomes
1. Having basic programming ability with MATLAB
2. Solving Systems of Linear Equations quickly and with alternative methods
3. To learn the application areas of Curve Fitting and to be able to choose and apply the appropriate curve fitting method according to the nature of the problem.
4. Being able to solve one-dimensional and vector root finding problems
5. To determine appropriate numerical derivative methods depending on various factors such as the amount of error calculation and to be able to calculate these derivatives.
6. To be able to calculate numerical integrals.
7. To be able to solve differential equations numerically, both initial and boundary value problems.
8. To be able to apply Numerical Solutions for Eigenvalue Problems.
9. To be able to select and apply statistical methods such as curve fitting and hypothesis tests according to the nature of the problem.
10. To have an idea about the basics of optimization.
Tentative Course Plan
Week 1: Introduction and introduction of department professors and program process
Week 2: Engineering Design and Engineering Ethics
Week 3: Engineering Design and Engineering Ethics
Week 4: Technical communication and correspondence
Week 5: Entrepreneurship
Week 6: Matlab Courses
Week 7: Matlab Courses
Week 8: Latex Courses
Week 9: Latex Courses
Week 10: Image Processing presentation
Week 11: Image Processing presentation
Week 12: -
Tentative Assesment Methods
• Midterm 35 %
• Final 40 %
• Assignments 15 %
• Participation 10 %
Program Outcome *
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Course Outcome
1 B B B B
2 A, B A, B A B B
3 A, B A, B A B B
4 A, B A, B A B B
5 A, B A, B A B B
6 A, B B B
7 A, B B B
8 A, B B B
9 A, B A, B A B B
10 A, B A, B A B B