This lecture goes over some fundamental definitions of statistics. This is needed for any rigorous analysis of machine learning algorithms. We will define random variable, sample space, probability, expectation, standard deviation and variance and go over examples of discrete and continuous probability distributions. We will specifically spend time on uniform, binomial and normal (Gaussian) distributions. We will briefly mention other distributions such as poisson, exponential, geometric and negative binomial but these are left to the viewers to understand them in detail.

## 6 comments

Comments are closed.

Awesome Question best vj

figured out your question in like 12 seconds … just pass till the last

card and you get 100% right answer.

nevermind …. its get a red card .. not pick the color of the next card

…. so you cant beat it … its 50 50 … my b

basics Very well explained …Thumbs up

he said you have a different number of red and black cards, its not 50-50

@35:40 while talking about expectancy, he said if the dice is fully loaded

as 0,0,1/2,1/2,0,0 still we get an average of 3.5, what if its fully loaded

as 1/2,1/2,0,0,0,0 I only get 1.5 how is it an average?