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 Engineering
  • Department of Computer Engineering (English)
  • Course Structure Diagram with Credits
  • Numeric Analysis for Computer Engineers
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title BİM204 - Numeric Analysis for Computer Engineers
Course Type Required Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 3.5
Course Instructor(s) DOÇENT MEHMET KOÇ
Mode of Delivery This course is normally delivered face to face. However, in special circumstances such as pandemics and natural disasters, it is carried out in synchronous and/or asynchronous distance education format.
Prerequisites There is no prerequisites and co-requisites for that course
Courses Recomended MAT805 Calculus1 and MAT806 Calculus2 courses are recommended to students who will take that course.
Recommended Reading List Advanced Engineering Mathematics 9th/19th ed. (Erwin Kreyzig)
Assessment methods and criteria Two written midterms and one written final exam
Work Placement
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Introduction - Number Systems
Week - 2 Binary Machine Numbers & IEEE-754 Format
Week - 3 Error & Error Types
Week - 4 Root Finding Problems: Bisection Method
Week - 5 Root Finding Problems: Fixed Point Iteration
Week - 6 Root Finding Problems: Newton's Method & Secant Method
Week - 7 Interpolation & Polynomial Approximation
Week - 8 Data Approximation & Neville's Method
Week - 9 Divided Differences Interpolation
Week - 10 Divided Differences Interpolation Examples
Week - 11 Regresyon Analizi - Lineer Regresyon
Week - 12 Linear Regression using Least Square Method
Week - 13 Linear Regression using R Squared Method

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Question & Answer
  • Demonstration
  • Drill - Practise
  • Competences
  • Productive
  • Rational
  • Effective use of a foreign language
  • Abstract analysis and synthesis
  • Problem solving
  • Organization and planning

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 30
Quiz 1 20
Final Exam 1 50
Toplam (%) 100
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