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.