Overview

This post will provide a technical guide on machine learning theory within data science interviews. It is by no means comprehensive but aims to highlight key technical points within each topic. The problems discussed are from this data science interview newsletter which features questions from top tech companies and will be involved in an upcoming book.

Mathematical Prerequisites

Random Variables

Random variables are a core topic within probability and statistics, and interviewers are generally looking for an understanding of the principles and basic ability to manipulate them.

For any given random variable X, it has the following properties (below we assume X is continuous, but the analogous holds for discrete random variables). The expectation (average value) is given by:

and the variance is given by:

For any given random variables X and Y, the covariance, a linear measure of relationship, is defined by:

and normalization of covariance is the correlation between X and Y:

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