Machine Learning (Part 1 of 5): Probability

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.


  1. d3th2u says:

    figured out your question in like 12 seconds … just pass till the last
    card and you get 100% right answer.

  2. d3th2u says:

    nevermind …. its get a red card .. not pick the color of the next card
    …. so you cant beat it … its 50 50 … my b

  3. srikanthlives says:

    @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?

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