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Engineering Probability Lectures, Fall 2018

Rensselaer Polytechnic Institute via YouTube

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Details

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Provider

YouTube
Pricing

Free Video
Languages

English
Effort

22 hours
Sessions

Self-Paced
Level

Intermediate

Found in

This course develops engineering probability from sample spaces and probability axioms through random variables, distributions, joint behavior, estimation, hypothesis testing, and limit theorems. It includes discrete and continuous models, Gaussian variables, Bayesian and maximum likelihood estimation, and distribution-fit testing.

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.