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 Infomation Sciences
  • 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: 30%Project: 30%Final Exam: 40%
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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