School of Engineering \ Electrical and Electronics Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
Electrical and Electronics Engineering Bachelor's Degree Program
AppIication areas of Numerical Methods, digital solution of truth equations, parts of parts of equations, digital derivative and integral, solution of initial and boundary value problems, eigenvalue problems, statistics, hypothesis testing, introduction to optimization, artificial intelligence. machine leaming.
Textbook and / or References
J. Kiusalaas, Numerical Methods in Engineering with MATLAB, Campridge Uni Press, 2009.
Teaching the fundamentals of numerical methods and machine learning used in solving various engineering problems frequently encountered in Electrical and Electronics Engineering.
1. Have basic programming ability with MATLAB.
2. Solve Systems of Linear Equations with alternative methods and quickly.
3. Solve one-dimensional and vector root finding problems.
4. Learns the application areas of Curve Fitting and selects and applies the appropriate curve fitting method according to the nature of the problem.
5. Determine appropriate numerical derivative methods depending on various factors such as the amount of error calculation and calculate these derivatives.
6. Can calculate numerical integration.
7. Solve numerically differential equations, both initial and boundary value problems.
8. Apply numerical solutions for eigenvalue problems.
9. Select and apply statistical methods such as curve fitting and hypothesis testing according to the nature of the problem.
10. Have an idea about the basics of optimisation.
11. Have an idea about artificial intelligence and machine learning. Understands machine learning approaches at a basic level.
12. Gains the ability to solve problems at the beginner level using machine learning.
Week 1: Linear Systems and Solutions
Week 2: Root Finding Methods
Week 3: Interpolation and Curve Fitting
Week 4 Numerical Derivative Calculation
Week 5 Numerical Integral Calculation
Week 6: Numerical Solutions of Initial Value Problems
Week 7: Numerical Solutions of Boundary Value Problems
Week 8: Numerical Solutions of Eigenvalue and Eigenvector Problems
Week 9: Statistics and Hypothesis Testing, Introduction to Optimisation
Week 10: Introduction to Artificial Intelligence and Machine Learning
Week 11: Basic concepts and elements of machine learning
Week 12: Modelling and problem solving approaches with machine learning approaches
Tentative Assesment Methods
• Midterm
• Final
• Assignments
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B
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B
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B
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A, B
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A, B
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A
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B
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4 |
A, B
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A, B
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A
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B
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B
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5 |
A, B
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A, B
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A
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B
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B
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6 |
A, B
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B
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B
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B
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B
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B
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B
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B
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12 |
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B
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