Since June 2026
Instructor since June 2026
Are you an engineering or mathematics (Bachelor's/Master's) student who finds numerical analysis too abstract or difficult to apply?
This course provides a rigorous yet practical introduction to scientific computing, showing how numerical methods are used to solve real engineering and scientific problems.
You'll first learn how numerical computations behave by studying floating-point arithmetic, rounding and truncation errors, conditioning, stability, and error propagation. These concepts are essential for understanding why numerical algorithms succeed—or fail.
Next, you'll learn how to solve nonlinear equations using the bisection method, fixed-point iteration, and Newton's method, before moving on to linear systems through both direct methods (Gaussian elimination, LU decomposition, Cholesky factorization) and iterative methods (Jacobi, Gauss-Seidel, and relaxation methods).
The second half of the course covers polynomial interpolation and approximation (Lagrange, Newton, Hermite, least-squares approximation, and splines), numerical differentiation and integration, and numerical methods for ordinary differential equations, including Euler's method, Runge-Kutta methods, and finite difference techniques.
Every algorithm is implemented step by step in Matlab so that you not only understand the mathematics behind it but also learn how to apply it to practical engineering problems. Throughout the course, we compare the accuracy, convergence, stability, and computational efficiency of the different numerical methods.
This course is ideal for students preparing for university exams, engineering projects, scientific computing courses, or anyone wishing to build solid foundations in numerical analysis.
Prerequisites: A good understanding of single-variable calculus, basic linear algebra, and elementary programming concepts is recommended.
This course provides a rigorous yet practical introduction to scientific computing, showing how numerical methods are used to solve real engineering and scientific problems.
You'll first learn how numerical computations behave by studying floating-point arithmetic, rounding and truncation errors, conditioning, stability, and error propagation. These concepts are essential for understanding why numerical algorithms succeed—or fail.
Next, you'll learn how to solve nonlinear equations using the bisection method, fixed-point iteration, and Newton's method, before moving on to linear systems through both direct methods (Gaussian elimination, LU decomposition, Cholesky factorization) and iterative methods (Jacobi, Gauss-Seidel, and relaxation methods).
The second half of the course covers polynomial interpolation and approximation (Lagrange, Newton, Hermite, least-squares approximation, and splines), numerical differentiation and integration, and numerical methods for ordinary differential equations, including Euler's method, Runge-Kutta methods, and finite difference techniques.
Every algorithm is implemented step by step in Matlab so that you not only understand the mathematics behind it but also learn how to apply it to practical engineering problems. Throughout the course, we compare the accuracy, convergence, stability, and computational efficiency of the different numerical methods.
This course is ideal for students preparing for university exams, engineering projects, scientific computing courses, or anyone wishing to build solid foundations in numerical analysis.
Prerequisites: A good understanding of single-variable calculus, basic linear algebra, and elementary programming concepts is recommended.
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