ECE 302 Probabilistic Methods in Electrical Engineering

Spring 2006

Course Information

  • Instructor:  Sarah Koskie

  • Email:

  • Lectures:   MW 9:30–10:45 am in SL 206

  • Office Hours: M11am–1pm, T1–3pm, or by appointment, in SL 164F

  • Textbook:   Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 2nd ed. by Roy D. Yates and David J. Goodman, Wiley, 2005.   

  • Grading:  
    • 15% Homework
    • 50% Midterm Exams (25% each)
    • 35% Final Exam (noncumulative)

  • Course Information Sheet    (Updated January 18, 2006)

  • Homework Assignments    (Updated April 10, 2006)

  • Homework Solutions    (Updated April 27, 2006)

  • Exam Solutions    (Updated March 18, 2006)

  • Practice Exams    (Updated May 04, 2006)

  • Some Useful Links:

  • Course Outcomes: Upon completion of the course, students should be able to:

    • Solve simple probability problems with electrical and computer engineering applications using the basic axioms of probability.    (Chapters 1 – 3)
    • Describe the fundamental properties of probability density functions with applications to single and multivariate random variables.    (Chapter 1 – 5)
    • Describe the functional characteristics of probability density functions frequently encountered in electrical and computer engineering such as the Binomial, Uniform, Gaussian and Poisson.    (Chapter 1 – 5)
    • Determine the first through fourth moments of any probability density function using the moment generating function.    (Chapter 6)
    • Calculate confidence intervals and levels of statistical significance using fundamental measures of expectation and variance for a given numerical data set.    (Chapters 7, 8)
    • Discern between random variables and random processes for given mathematical functions and numerical data sets.    (Chapter 10)
    • Determine the power spectral density of a random process for given mathematical functions and numerical data sets.    (Chapter 11)
    • Determine whether a random process is ergodic or nonergodic and demonstrate an ability to quantify the level of correlation between sets of random processes for given mathematical functions and numerical data sets.    (Chapter 10)
    • Model complex families of signals by means of random processes.    (Chapter 10)
    • Determine the random process model for the output of a linear system when the system and input random process models are known.    (Chapter 11)

Page last modified 03/05/07.