
Probability and Random Processes


Eligibility
12+

Duration
6 months

Course Outline
Introduction: Why probability models? Sources of randomness. Examples and application of probability models in EECS.
Foundations: Sample space, events, axioms of probability. Conditional probability, independence. Sequential experiments.
Discrete Random variables: Definition. Probability mass functions. Functions of random variables. Expectations. Joint probability mass functions of multiple random variables. Conditional distribution. Independent random variables. Important distributions.
Further topics on random variables: Moment generating functions. Sums of
random variables. Conditional expectation.
Working Area
Probability and Random Processes Technician
Probability and Random Processes Engineer
Techincal Head
etc.
