Week 1:
* Sample Space, Events and Probability
* Conditional Probability and Independence
Week 2:
* Independence and Bernoulli Trials
* Poisson Approximation
Week 3:
* Sampling without Replacement
* Discrete Random Variables
Week 4:
* Discrete Random Variables:
o Distribution, Probability Mass function
o Joint, Marginal, and Conditional
o Independence
Week 5:
* Discrete Random Variables
o Expectation and Variance
o Correlation and Covariance
Week 6:
* Markov and Chebyschev Inequalties
* Conditional Expectation, Conditional Variance
Week 7:
* Uncountable Sample Spaces
* Probability Densities
* Continuous Random Variables: Uniform Distribution
Week 8:
* Exponential Random and Normal Random Variable
* Joint Density, Marginal and Conditional Density
Week 9:
* Independence
* Functions of Random Variables
Week 10:
* Sums of Independent Random variables
* Distributions of Quotients of Independent Random variables.
Week 11:
* Law of Large Numbers (without Proof)
Week 12:
* Central Limit Theorem (without Proof)
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