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Syllabus ( ELEC 218 )


   Basic information
Course title: Probability and Randomness
Course code: ELEC 218
Lecturer: Assist. Prof. Saliha BÜYÜKÇORAK EDİBALİ
ECTS credits: 5
GTU credits: 3 (3+0+0)
Year, Semester: 2, Spring
Level of course: First Cycle (Undergraduate)
Type of course: Compulsory
Language of instruction: Turkish
Mode of delivery: Face to face , Group study
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: This course will present the fundamental concepts of probability and statistics from an engineering prospective, emphasizing applications.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Apply the fundamental concepts of probability and statistics to real-world engineering problems.

    Contribution to Program Outcomes

    1. Apply the mathematical, scientific and engineering knowledge for real life problems
    2. Formulate and solve engineering problems

    Method of assessment

    1. Homework assignment
  2. Construct the probability distributions of random variables based on real-life scientific scenarios and data sets, and then use it to find expectation and variance.

    Contribution to Program Outcomes

    1. Apply the mathematical, scientific and engineering knowledge for real life problems
    2. Formulate and solve engineering problems

    Method of assessment

    1. Written exam
    2. Homework assignment
  3. Explain the fundamental concepts of probability theory.

    Contribution to Program Outcomes

    1. Obtain basic knowledge of Electronics Engineering.

    Method of assessment

    1. Written exam
  4. Characterize random processes with an emphasis on stationary random processes

    Contribution to Program Outcomes

    1. Obtain basic knowledge of Electronics Engineering.

    Method of assessment

    1. Written exam
   Contents Up
Week 1: Basic principle of counting, permutation, combination
Week 2: Axioms of probability, conditional probability
Week 3: Independence, chain rule, Bayes rule, random variables
Week 4: Discrete random variables: Probability mass and cummulative distribution functions, expected value and variance
Week 5: Bernoulli, binom, Poisson and geometric random variables
Week 6: Continuous random variables: Probability density and cummulative distribution functions, expected value and variance
Week 7: Uniform, normal and exponential random variables
Week 8: Midterm exam
Week 9: A function of random variables, jointly distributed random variables
Week 10: Marginal and conditional densities, correlation and covariance
Week 11: Sum of independent random variables, linear mean square error estimation
Week 12: Conditional expectation, moment generating functions
Week 13: Limit theorems: Markov and Chebyshev inequilities, weak law of large numbers, central limit theorem
Week 14: Random processes: Auto-correlation and covariance functions, power spectral density, response of linear time-invariant systems to random processes
Week 15*: -
Week 16*: Final Exam
Textbooks and materials: [1] Probability of Statistics for Engineering & Scientists. Walpole E.W., Myers R.H., Myers S.L., Ye K. Pearson Education, Prentice Hall Inc.

[2] Probability and Random Processes with Applications to Signal Processing and Communications, Scott L. Miller, Donald G. Childers
Recommended readings: [1] A First Course in Probability, S. Ross, Prentice Hall

[2] Probability Random Variables and Stochastic Processes, A.Papolis, McGraw Hill
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 8 25
Other in-term studies: 0
Project: 0
Homework: 2, 3, 4, 6, 7, 9, 10, 11, 12 20
Quiz: 5 5
Final exam: 16 50
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 2 14
Practice, Recitation: 0 0
Homework: 3 9
Term project: 0 0
Term project presentation: 0 0
Quiz: 2 1
Own study for mid-term exam: 11 1
Mid-term: 2 1
Personal studies for final exam: 11 1
Final exam: 2 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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