Overview Machine Learning deals all the time with predicting a certain outcome, given a set of inputs, or features. The essence of this comes from conditional probability, which can be used to calculate the expectation of events which are dependent on other(s).
Overview In the last post (see here) I explained the following discrete distributions:
Uniform Bernoulli Binomial In this post, we continue on this same subject, but now on Multinoulli and Multinomial distributions.
Overview Hello! In this post we will go over the concept of discrete random variables in the context of Probability Theory. How is this concept relevant for fields such as Machine Learning and Artificial Intelligence?
Hello! in this post you will learn about some common discrete distributions. Distribution can be related with the “behavior”, of a variable, what is the probability associated with a certain event $E$ under a certain distribution $F$.
Overview It can be said that probability is one of the foundations of machine learning, together with linear algebra and calculus. In this post I would like to provide a basic description of what probability means in an intuitive sense, followed by some essential concepts in probability theory.