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ELE 3310 - Introduction to Probability and Random Processes

3 lecture hours 0 lab hours 3 credits
Course Description
This course is an introduction to probability and stochastic (random) processes for electrical engineering students.  The course begins with an introduction to fundamental probability concepts, such as set theory, conditioning, independence, and counting methods.  Random variables, such as binomial, uniform, and Gaussian, and their distributions are then introduced to model the outcomes of experiments with countable or measurable quantities.  Foundational statistical concepts are covered through examining the sample mean and variance estimators, the central limit theorem, and law of large numbers. Random processes are introduced to model uncertainty in electrical signals, noise, and processing by linear filters. Examples are drawn from signal processing, communications, reliability, and engineering decision-making. 
Prereq: ELE 3300  (quarter system prereq: EE 3032)
Note: None
This course meets the following Raider Core CLO Requirement: None
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Apply fundamental concepts of probability theory and statistics to the solution of electrical engineering problems involving uncertainty
  • Interpret and calculate expectations and probabilities involving single and multiple random variables
  • Model uncertainty in electrical engineering problems using common probability mass and density functions such as Bernoulli, binomial, geometric, uniform, Poisson, exponential, and Gaussian
  • Interpret and apply the Central Limit Theorem and the Law of Large Numbers
  • Apply classical estimation principles to the sample mean and sample variance estimators
  • Determine properties and characteristics of a random process
  • Apply random process theory to linear filtering of random signals
Prerequisites by Topic
  • Calculus
  • Fourier transforms
  • Linear and time-invariant systems
Course Topics
  • Basic probability concepts
  • Single random variables, continuous and discrete
  • Multiple random variables
  • Conditioning random variables
  • Statistical concepts
  • Random processes, noise, and filtering
Coordinator
Dr. Jay Wierer

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