Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty Of Computer And Information Technologies
  • Artificial Intelligence And Machine Learning Pr. (English)
  • Course Structure Diagram with Credits
  • Scientific Computing Techniques
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information
Code - Course Title YZM2001 - Scientific Computing Techniques
Course Type Required Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 5.0
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ NİHAL İNCE
Mode of Delivery This course is offered exclusively in an in-person format.
Prerequisites There are no prerequisites or co-requisites for this course.
Courses Recomended There are no other recommended courses related to this one.
Required or Recommended Resources Heath, M. T. (2018). Scientific Computing: An Introductory Survey (Revised 2nd Edition). Society for Industrial and Applied Mathematics. Burden, R. L., Faires, J. D., & Burden, A. M. (2015). Numerical Analysis (10th Edition). Cengage Learning. Kong, Q., Siauw, T., & Bayen, A. (2020). Python Programming and Numerical Methods: A Guide for Engineers and Scientists (1st Edition). Academic Press.
Recommended Reading List Heath, M. T. (2018). Scientific Computing: An Introductory Survey (Revised 2nd Edition). Society for Industrial and Applied Mathematics. Burden, R. L., Faires, J. D., & Burden, A. M. (2015). Numerical Analysis (10th Edition). Cengage Learning. Kong, Q., Siauw, T., & Bayen, A. (2020). Python Programming and Numerical Methods: A Guide for Engineers and Scientists (1st Edition). Academic Press.
Assessment methods and criteria Midterm exam, term project, final exam
Work Placement This is not suitable for this class.
Sustainability Development Goals

Content

Weeks Topics
Week - 1 What is Scientific Computing? The Python Ecosystem. Concepts of Numerical Error (Round-off, Truncation)
Week - 2 Scientific Computing in Python I: NumPy Deep Dive (Vectorization, Broadcasting)
Week - 3 Scientific Computing in Python II: Data Visualization. Basic plotting with Matplotlib (plot, scatter, histogram)
Week - 4 Solutions of Nonlinear Equations: The Bisection Method
Week - 5 Solutions of Nonlinear Equations II: The Newton-Raphson Method
Week - 6 Systems of Linear Equations I: Matrices and Vectors
Week - 7 Systems of Linear Equations II: LU Decomposition concept and application
Week - 8 Interpolation I: Concept. Lagrange Interpolation
Week - 9 Interpolation II: Spline Interpolation
Week - 10 Curve Fitting: Method of Least Squares
Week - 11 Numerical Differentiation: Finite Differences (Forward, Backward, Central)
Week - 12 Numerical Integration I: The Trapezoidal Rule
Week - 13 Numerical Integration II: Simpson's Rule
Week - 14 Solving ODEs: Euler's Method

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Question & Answer
  • Demonstration
  • Drill - Practise
  • Problem Solving
  • Report Preparation and/or Presentation
  • Competences
  • Rational
  • Questoning
  • Abstract analysis and synthesis
  • Problem solving
  • To work autonomously
  • Elementary computing skills
  • To work in interdisciplinary projects

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 30
Project 1 30
Final Exam 1 40
Toplam (%) 100
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