Engineering Probability

Fall 2019

Lecture 1

experiments, sample space, events

Lecture 2

axioms, probabilistic models, counting methods​

Lecture 3

Conditional probability

Lecture 4

Baye's rule, independence, Bernoulli trials​

Lecture 5

Discrete random variables


Lecture 6

Expected value and moments 

Lecture 7

Conditional probability mass functions

Lecture 8

Cumulative distribution functions

Lecture 9

Lecture 10

Probability distribution functions, continuous random variables

Lecture 11

The Gaussian random variable, Q function 

Lecture 12

Expectations of a random variable 

Lecture 13

Functions of a random variable; inequalities


Lecture 14

Two random variables (discrete)

Lecture 15

Two random variables (continuous); independence

Lecture 16

Joint expectations, correlation, covariance

Lecture 17

Conditional PDFs; Bayesian and maximum likelihood estimation

Lecture 18

Lecture 19

Conditional expectations

Lecture 20

Sums of random variables

Lecture 21

Central limit theorem; confidence intervals

Lecture 22

MAP, ML, and MMSE estimation

Lecture 23

Hypothesis testing

Lecture 24

Testing the fit of a distribution; generating random samples

Lecture 25


Lecture 26


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