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
  • Graduate School of Sciences
  • Department of Computer Engineering
  • Doctorate Degree (Ph.D)
  • Course Structure Diagram with Credits
  • Advanced Engineering Mathematics
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title BİL623 - Advanced Engineering Mathematics
Course Type Elective Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ CAHİT PERKGÖZ
Mode of Delivery Face to face
Prerequisites None
Courses Recomended None
Required or Recommended Resources Matrix Analysis and Applied Linear Algebra, SIAM, Carl D. Meyer, 2010Signals and Systems, Prentice Hall, Alan V. Oppenheim, Alan S. Willsky, 1996Computational Bayesian Statistics, An Introduction, Cambridge University Press, M. A. A. Turkman, C. D. Paulino, P. Müller, 2019
Recommended Reading List None
Assessment methods and criteria 2 Midterm exams, 1 Final exam, 1 Homework assignment and 4 Quizzes.
Work Placement Not applicable
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Linear Equations, Laplace Transforms
Week - 2 Rectangular Systems and Echelon Forms
Week - 3 Matrix Algebra
Week - 4 Vector Spaces
Week - 5 Norms, Inner Products, and Orthogonality
Week - 6 Gram-Schmidt Procedure
Week - 7 Discrete Time Signals, Fourier Analysis
Week - 8 Fourier Transforms
Week - 9 Time Frequency Analysis, Z-Transforms and Filters
Week - 10 Determinants
Week - 11 Eigen values, Eigenvectors
Week - 12 Probability
Week - 13 Bayesian Learning Methods, Markov Chain, Monte Carlo Method
Week - 14 Optimization Methods

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Drill - Practise
  • Problem Solving
  • Competences
  • Rational
  • Questoning
  • Abstract analysis and synthesis
  • Problem solving
  • To work autonomously
  • Elementary computing skills

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 40
Final Exam 1 60
Toplam (%) 100
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