notes.txt

Be able to:
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Define probability using the law of large numbers

Quantify uncertain events using:
The addition rule
The product rule
Bayes’ Theorem
Tree Diagrams

Describe random events using probability distributions

Understand use-cases for the following probability distributions:
Binomial Distribution
Poisson Distribution

Describe a population using a sampling distribution and the central limit theorem
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Set Theory:
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https://www.codecademy.com/paths/machine-learning-ai-engineering-foundations/tracks/mlef-math-and-statistics-for-ml-ai-engineers/modules/mlef-probability-for-mlaieng/articles/probability-set-theory-and-the-law-of-large-numbers

Set theory is a branch of mathematics based around the concept of sets. In simple terms, a set is a collection of things.

Notationally, mathematicians often represent sets with curly braces. Sets also follow two key rules:
Each element in a set is distinct.
The elements in a set are in no particular order. {1,2,3,4,5} == {5,3,2,4,1}
When defining a set, we often use a capital letter. A = {1,2,3,4,5}

Sets can also contain subsets. Set A is a subset of set B if all the elements in A exist within B.

A sample point is a single possible outcome of an experiment. Finally, a sample space is the set of all possible sample points for an experiment.

A specific outcome (or set of outcomes) is known as an event and is a subset of the sample space.

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