I am currently doing the advent of code 2021 programming challenge, which (at the time I’m writing this), is on day 7. I got the motivation to write this post from this tweet that had some questions about the solution. I am going to attempt to explain my approach to this problem and why we find this particular solution, so if you have not seen the problem I encourage it to try it yourself before reading this (have fun!

What is the Collatz conjecture ?

Let us choose any positive integer \(k>0\), let us then apply the following rules to \(k\) repeatedly:

Today I want to explain linear regression. It is one of the simplest statistical learning models and can be implemented in only a couple lines of Python code, in an efficient manner. Being so simple however does not mean it is not useful, in fact it can be very practical to explore relationships between features in a dataset and make predictions on a target value. Therefore I think it’s important to understand how the method works and how the different parameters have an effect on the outcome.

This is a little writeup of a project I did in collaboration with a classmate while studying a algorithmic complexity class. We implemented a faster, but still exact, \(k\) nearest neighbors classifier based on k-d trees. I learned a lot and hope this can be interesting to some of you.

This is Part 2. of my decision tree series. Here we will see how we can build a decision tree algorithmically using Leo Breiman’s (One of the big, big names in decision trees) CART algorithm.

The first subject I want to tackle on this page is decision trees. What are they? How do they work? How can I make one?
I am planning to make a small series, ranging from explaining the concept, to implementing a decision tree inference algorithm and hopefully all the way up to implementing Random Forests.
All right let’s get started.