ECTS - Computational Methods in Electrical and Electronics Engineering

Computational Methods in Electrical and Electronics Engineering (EE506) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Computational Methods in Electrical and Electronics Engineering EE506 3 0 0 3 5
Pre-requisite Course(s)
EE 350, MATH 276
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Discussion, Question and Answer, Drill and Practice.
Course Coordinator
Course Lecturer(s)
  • Assoc. Prof. Dr. Kemal Efe Eseller
Course Assistants
Course Objectives The aim of this course is to review the basic numerical methods in engineering and to teach advanced computational methodologies which is to be beneficial in engineering research. The course is expected to make the graduate students able to solve the complex problems such as numerical solution of differential equation, optimization and statistical analysis which are frequently encountered in graduate level research in electrical and electronics engineering.
Course Learning Outcomes The students who succeeded in this course;
  • - Ability to use direct and iterative methods in the solution of system of linear equations - Ability to use and implement statistical methods - Ability to construct polynomial approximations to functions by interpolation and extrapolation - Ability to implement linear transforms - Ability to use MATLAB to implement numerical methods - Ability to use and implement optimization techniques
Course Content Root finding and numerical integration, fixed and floating point arithmetic and error standards, one and multidimensional interpolation and extrapolation, numerical optimization techniques, least squares, statistical methods (Monte Carlo), computational approaches to linear transformations (Karhunen-Loeve, discrete Fourier).

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to MATLAB and basic rules of the software -
2 Review of basic numerical methods (root finding, numerical integration, etc.) Review previous week's notes
3 Review of basic numerical methods (root finding, numerical integration, etc.) Review previous week's notes
4 Fixed and floating point arithmetic, number representations, IEEE floating-point standard, error propagation, forward error analysis of primitive operations Review previous week's notes
5 Interpolation and extrapolation (linear and polynomial interpolation in 1-D, 2-D and 3-D) Review previous week's notes
6 Solutions of linear algebraic equations with different methods Review previous week's notes
7 Solutions of linear algebraic equations with different methods Review previous week's notes
8 Midterm Examination (including a MATLAB test) Review previous week's notes
9 Numerical approaches to optimization (gradient methods, handling the constraints, Lagrange multipliers) Review previous week's notes
10 Numerical approaches to optimization (gradient methods, handling the constraints, Lagrange multipliers) Review previous week's notes
11 Modeling of data (review of least squares) Review previous week's notes
12 Statistical methods (Monte Carlo methods) Review previous week's notes
13 Linear transforms (Karhunen-Loeve transform, independent component analysis) Review previous week's notes
14 1-D and 2-D discrete Fourier transform (DFT) Review previous week's notes
15 Project Presentations Review of topics
16 Final Examination period Review of topics

Sources

Course Book 1. Steven Chapra, Raymond Canale, “Numerical Methods for Engineers”, McGraw-Hill, 6th Edition, 2009
2. F. B. Hildebrand , “Introduction to Numerical Analysis”, Dover, 2nd Edition, 1987
3. H. Mathews, K.D. Fink, “Numerical Methods Using Matlab”, Pearson, 4th Edition, 2004

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 10 20
Presentation - -
Project 1 20
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 30
Final Exam/Final Jury 1 30
Toplam 14 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Ability to apply knowledge on Mathematics, Science and Engineering to advanced systems.
2 Implementing long-term research and development studies in major areas of Electrical and Electronics Engineering.
3 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications.
4 Graduating researchers active on innovation and entrepreneurship.
5 Ability to report and present research results effectively.
6 Increasing the performance on accessing information resources and on following recent developments in science and technology.
7 An understanding of professional and ethical responsibility.
8 Increasing the performance on effective communications in both Turkish and English.
9 Increasing the performance on project management.
10 Ability to work successfully at project teams in interdisciplinary fields.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 14 3 42
Presentation/Seminar Prepration
Project 1 20 20
Report
Homework Assignments 5 3 15
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury
Prepration of Final Exams/Final Jury
Total Workload 125