Syllabus Application
ENS 311
Numerical Analysis
Faculty
Faculty of Engineering and Natural Sciences
Semester
Spring 2025-2026
Course
ENS 311 -
Numerical Analysis
Time/Place
Time
Week Day
Place
Date
10:40-11:30
Mon
FENS-L047
Feb 16-May 22, 2026
10:40-12:30
Tue
FENS-G029
Feb 16-May 22, 2026
Level of course
Undergraduate
Course Credits
SU Credit:3, ECTS:6, Basic:2, Engineering:4
Prerequisites
CS 201 or DSA 201
Corequisites
Course Type
Lecture
Instructor(s) Information
Melih Türkseven
Course Information
Catalog Course Description
This course covers the use of numerical computing techniques for mathematical and scientific problems. Topics include: approximations and computer arithmetic, error analysis, conditioning and stability, Taylor series, finding the roots of nonlinear equations, eigenvalue problems, iterative methods for solving system of linear equations such as Jacobi and Gauss-Seidel methods, numerical optimization methods and their applications, Newton's and Lagrange’s polynomials for interpolation, curve fitting, basic statistics, correlation, characterization of regression model accuracy, numerical derivation and integration, solutions to ordinary differential equations.
Course Learning Outcomes:
| 1. | 1. Analyze numerical errors, such as round-off, truncation, and cancellation, and develop computational tools to solve numerical problems effectively. |
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| 2. | 2. Use iterative methods for solving both nonlinear equations and system of linear equations; compare direct and iterative solutions for system of linear equations. |
| 3. | 3. Apply polynomial interpolation and least squares regression techniques; and distinguish their appropriate applications. |
| 4. | 4. Compute numerical derivatives and integrals, and solve ordinary differential equations using numerical methods. |
| 5. | 5. Apply basic statistical techniques to characterize data and assess regression models. |
| 6. | 6. Apply basic optimization principles and numerical optimization methods to solve practical engineering problems. |
Course Objective
This course covers the use of numerical computing techniques for mathematical and scientific problems. Topics include: floating-point representation, error analysis, conditioning and stability, root finding, numerical optimization, Newton's method, curve fitting and interpolation, solution to systems of linear equations using techniques such as LU decomposition, Gaussian elimination, Jacobi, Gauss-Seidel iteration, eigenvalue problems, SVD, numerical integration and solutions to differential equations.
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Course Materials
Resources:
Numerical Analysis, J. Douglas Faires and Richard L. Burden, Thomson Press, 2004