This course covers the fundamentals of Engineering Probability, including experiments, sample spaces, events, axioms of probability, counting methods, conditional probability, independent events, random variables (discrete and continuous), expected value, moments, cumulative distribution functions, Gaussian random variables, joint expectations, correlation, covariance, hypothesis testing, and more. The course teaches skills such as calculating probabilities, working with random variables, estimating parameters, and testing statistical hypotheses. The teaching method consists of lectures following a structured syllabus. This course is intended for students or professionals in engineering, particularly those in electrical engineering, who want to gain a solid understanding of probability theory and its applications in the field.
Overview
Syllabus
Engineering Probability Lecture 1: Experiments, Sample Spaces, and Events.
Engineering Probability Lecture 2: Axioms of probability and counting methods.
Engineering Probability Lecture 3: Conditional probability.
Engineering Probability Lecture 4: Independent events and Bernoulli trials.
Engineering Probability Lecture 5: Discrete random variables.
Engineering Probability Lecture 6: Expected value and moments.
Engineering Probability Lecture 7: Conditional probability mass functions.
Engineering Probability Lecture 8: Cumulative distribution functions (CDFs).
Engineering Probability Lecture 9: Probability density functions and continuous random variables.
Engineering Probability Lecture 10: The Gaussian random variable and Q function.
Engineering Probability Lecture 11: Expected value for continuous random variables.
Engineering Probability Lecture 12: Functions of a random variable; inequalities.
Engineering Probability Lecture 13: Two random variables (discrete).
Engineering Probability Lecture 14: Two random variables (continuous); independence.
Engineering Probability Lecture 15: Joint expectations; correlation and covariance.
Engineering Probability Lecture 16: Conditional PDFs; Bayesian and maximum likelihood estimation.
Engineering Probability Lecture 17: Conditional expectations.
Engineering Probability Lecture 18: Sums of random variables and laws of large numbers.
Engineering Probability Lecture 19: The Central Limit Theorem.
Engineering Probability Lecture 20: MAP, ML, and MMSE estimation.
Engineering Probability Lecture 21: Hypothesis testing.
Engineering Probability Lecture 22: Testing the fit of a distribution; generating random samples.
Taught by
Rich Radke
Tags
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