The mathematics rubric includes articles that focus primarily upon the mathematical underpinnings of machine learning, data analytics and cognitive computing. These articles either cover mathematical notation extensively or deal with mathematical principles that influence the code and models used in data science.
Machine learning algorithms are based on correlation – they do not specify cause and effect relations. Increasingly, Hence, Causality (cause and effect relations) is an… Read More »Understanding the Value of Bayesian Networks
I wrote this article for machine learning and analytic professionals in general. Actually, I describe a new visual, simple, intuitive method for supervised classification. It… Read More »The Riemann Hypothesis in One Picture
Machine learning and deep learning have become standard tools in a data scientist’s toolbox, applied to generate insights into large amounts of data. But organizations… Read More »DSC Podcast Series – Why and How to Partner Machine Learning with Math Optimization.mp4
As business decisions are becoming more and more complicated, Decision Analytics are becoming increasingly important and a way analytics teams can differentiate themselves from the… Read More »DSC Podcast Series – Navigating Decision Trade offs – Scratching the Surface with Math Optimization.mp4
While nowadays everyone talk about machine learning, data science and AI, few are mentioning statistical science. Its association, The American Statistical Association, founded in 1839,… Read More »New $1 Million Biennial Prize to Revitalize Statistics
I saw a nice representation of distance metrics. This topic is not easy to explain. This representation provides a good overview (link below to download… Read More »Understanding Distance Metrics and Their Significance
This tutorial explains how to build confidence regions (the 2D version of a confidence interval) using as little statistical theory as possible. I also avoid… Read More »Dual Confidence Regions: A Simple Introduction
In part 1 here, we introduced an original class of point processes, generalizing the Poisson process which is a limiting case, in a simple and intuitive… Read More »Machine Learning Inference for Point Processes: A General, Simple Introduction with Simulations
Update: Part 2 of this article is now available, here. In this article, original stochastic processes are introduced. They may constitute one of the simplest… Read More »A Gentle, Original Approach to Stochastic Point Processes
The probability distributions introduced in this article generalize what is known as infinite convolutions of centered Bernoulli or Rademacher distributions. Some of them are singular… Read More »A New Class of Non-standard Probability Distributions
