Course Details

Return to Classes
Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1ECE551SCIENTIFIC COMPUTING WITH MATLAB3+0+037,521.09.2025

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program ELECTRICAL AND COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course To help the students learn the details of MATLAB and practice them in various types of problems.
To help students acquire theoretical concepts in scientific computing or numerical techniques and apply them in MATLAB.
Course Content The course uses the theoretical concepts in scientific computing to help students excel in MATLAB programming environment. Starting from basic programming rules and syntax in MATLAB, students learn root finding methods and solutions of linear equations and implement them in MATLAB. Curve fitting, interpolation, optimization, numerical derivation and integration methods are discussed and implemented. Finally, students develop a graphical user interface which is related to the solution of a problem in their own research fields.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator Research Assist.Dr. Sinan Genç sinan.genc@agu.edu.tr
Name of Lecturers Research Assist.Dr. Sinan Genç sinan.genc@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources -Essential MATLAB for Engineers and Scientists, B.H.Hahn, D.T.Valentine, Academic Press, Elsevier. -Engineering and Scientific Computations Using MATLAB, S.E.Lyshevski, WILEY. -MATLAB Differential Equations, C.S.Lopez, Apress, Springer. -Numerical Methods using MATLAB, A.Gupta, Apress, Springer. -MATLAB Programming for Numerical Analysis, C.S.Lopez, Apress, Springer.

Course Category
Engineering %0
Science %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Quiz/Küçük Sınav 4 % 20
Ödev 4 % 20
Proje/Çizim 1 % 20
Final examination 1 % 25
Diğer (Staj vb.) 1 % 15
Total
11
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Yazılı Sınav 1 3 3
Ev Ödevi 4 5 20
Sınıf İçi Aktivitesi 13 1 13
Sunum için Hazırlık 4 3 12
Sunum 1 10 10
Proje 1 20 20
Kısa Sınav 4 1 4
Kişisel Çalışma 13 3 39
Ders dışı çalışma 2 30 60
Yüz Yüze Ders 13 3 39
Final Sınavı 1 3 3
Total Work Load   Number of ECTS Credits 7,5 223

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Apply concepts like scripts, variables, plots, vectors, matrices, indexing, functions, for and while loops, structure and cell arrays, and debugging.
2 Evaluate root-finding methods and apply them in various mathematical functions in MATLAB.
3 Apply approaches used in solving systems of linear equations, least-square fitting of a curve to data, interpolation, numerical integration and derivation, and optimization in MATLAB.
4 Develop a graphical user interface related to a specific research area.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to MATLAB: MATLAB environment, syntax, workspace, help/documentation. Scripts vs. functions. Variables, data types, and operators.
2 MATLAB Programming Fundamentals: Vectors and matrices, indexing, array operations. Loops (for, while), conditional statements. Debugging and error handling.
3 Plotting and Data Visualization: 2D and 3D plotting. Formatting figures, multiple plots, annotations. Data import/export.
4 Root-Finding Methods (Theory & MATLAB): Bisection, Newton-Raphson, Secant methods. Implementation and convergence analysis in MATLAB.
5 Systems of Linear Equations: Gaussian elimination, LU decomposition, iterative solvers (Jacobi, Gauss-Seidel). Matrix conditioning and error considerations.
6 Midterm
7 Spring Break
8 Curve Fitting and Least-Squares Methods: Polynomial fitting. Linear and nonlinear regression. Goodness-of-fit metrics.
9 Interpolation Methods: Lagrange and Newton interpolation. Spline interpolation. Practical MATLAB implementations.
10 Numerical Differentiation and Integration: Finite difference approximations. Trapezoidal and Simpson’s rule. MATLAB implementations and error analysis.
11 Optimization Techniques: Unconstrained optimization: gradient descent, Newton’s method. MATLAB’s fminsearch and fminunc functions. Applications in engineering problems.
12 Advanced MATLAB Programming Concepts: Structure arrays, cell arrays. File handling, modular programming. Efficiency considerations and vectorization.
13 Graphical User Interfaces (GUI) with MATLAB: Introduction to App Designer and GUIDE. Layouts, controls, callbacks. Simple example projects.
14 Project Development Workshop: Students define a research-related problem. Start implementing GUI + numerical method integration. Individual project support and feedback.
15 Final Project Presentations Student presentations of MATLAB GUIs. Peer and instructor feedback. Wrap-up: review of key concepts and applications.

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
C1 3 5 2 2 3 4 4 4 2 2 3
C2 3 4 3 3 4 4 4 3 2 2 3
C3 4 5 4 3 5 5 5 4 3 3 4
C4 4 3 3 4 4 5 5 5 4 3 4

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77562&lang=en
Return to Classes