The arguments / discussions between the Bayesian vs frequentist approaches in statistics are long running. I am interested in how these approaches impact machine learning. Often, books on machine learning combine the two approaches, or in some cases, take only one approach. This does not help from a learning standpoint. So, in this two-part blog we first discuss the differences between the Frequentist and Bayesian approaches.

Sampled from a distribution: Many machine learning algorithms make assumptions that the data is sampled from a frequency. For example, linear regression assumes gaussian distribution and logistic regression assumes that the data is sampled from a Bernoulli distribution.