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
  • Depart. of Electrical and Electronics Engineering
  • MS Program in Electronics and Electric Engineering
  • Program in Electrial Machinery
  • Course Structure Diagram with Credits
  • Random Variables and Stochastic Processes
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title EEM504 - Random Variables and Stochastic Processes
Course Type Required Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s)
Mode of Delivery This course is using face to face education method.
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended Students are advised to take Probability course at undergraduate level.
Required or Recommended Resources Probability, Random Variables and Stochastic Processes, Athanasios Papoulis, 1984.Fundamentals of Probability and Statistics for Engineers, TT Song, New York, John Wiley , 2004
Recommended Reading List Signal Detection and Estimation, Mourad Barkat, Artech House Radar Library, 2005.
Assessment methods and criteria 2 midterms, 2 homeworks and 1 final exam.
Work Placement Not Applicable
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Probability concepts, axioms, counting
Week - 2 Random variables
Week - 3 Expected values, moments, conditional probability
Week - 4 Functions of one random variables
Week - 5 Functions of two random variables
Week - 6 Midterm 2 week
Week - 7 Distributions and examples of distributions in discrete and continuous forms
Week - 8 Random processes
Week - 9 Random processes
Week - 10 Rassal süreçler
Week - 11 Mean and autocorrelation
Week - 12 Midterm 2 week
Week - 13 Linear transformation, linear filters, spectral density
Week - 14 Introduction to detection

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Drill - Practise
  • Case Study
  • Problem Solving
  • Competences
  • Productive
  • True to core values
  • Questoning
  • Effective use of a foreign language
  • Use time effectively
  • Abstract analysis and synthesis
  • Problem solving
  • To work autonomously
  • Elementary computing skills

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
Toplam (%) 0
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