Consider an $$n+1$$ dimensional matrix as follows:

$$ A = \begin{pmatrix} \frac{\pi^2}{6} & 1 & \frac{1}{4} & ... & \frac{1}{n^2} \\ 1 & \frac{\pi^2}{6} & \frac{1}{4} & ... & \frac{1}{n^2} \\ \vdots & …

Almost always we hear about classification or machine learning problems, the go-to methods to solve the problem are neural networks, or multi-layered percetrons (MLP). Now function approximation …

When I took the course on Real Analysis, it made me very nervous. I was having fever dreams from the past when I took undergraduate real analysis and the epsilon-delta definitions just did not make a …

In my last post I touched upon the intuition behind topological compactness. We as engineers often hear about the word ‘compact’ as a soft gatekeeping tool from doing serious …

This is a non-mathematical note on what I understand about compactness and what it means for a set or a space to be compact. The open cover definition is one that can be found in any textbook, but …

It might seem out of place that in the world of big data, finding a singular parameter’s estimate could be interesting. Quite the contrary – point estimation problems are one of the most …