TUTORIALS
This page presents a few tutorials on topics I find particularly interesting. I present a mix of applied and theoretical material, though these subjects should be of interest to anyone with a statistics or computer science background. PDFs can be opened directly, and I've included instructions for other file types.
Kernel Ridge Regression
Ridge regression is a powerful linear regression technique. However, this method may be augmented via a simple trick: the kernel trick. Take a look by saving the file below and uploading it to Google Colab.
Parallel Breadth-First Search
Breadth-first search is an algorithm used to search graph and tree data structures. In this tutorial, I explore its parallel version, which offers a simple but scaleable alternative to Dijkstra's algorithm. Try saving the file below and opening it in Google Colab.
The Ridge Regression Estimator's Variance
Ridge regression is a common technique used to estimate linear regression coefficients. This tutorial provides some insight as to why this method is so powerful.
The Tower Rule
The tower rule offers a neat trick to compute expectations. This tutorial provides an informal proof of the property and a simple example.
