for fun and profit! — Introduction In this post, I’m going to detail the techniques I used to win the Machine Learning Static Evasion Competition announced at this year’s DEFCON AI Village. The goal of the competition was to get 50 malicious Windows Portable Executable (PE) files to evade detection by three machine learning malware classifiers…

Information Theory — Introduction This is part 3 in a series on topics I consider fundamental to machine learning. These topics act as building blocks for gaining a deeper understanding of the field. So far we have covered: Probability Theory Maximum Likelihood Estimation The purpose of this post is to cover important concepts from…

Foundation and Application — Introduction In this post, we’ll cover the ins and outs of spectral clustering for graphs and other data. Clustering is one of the main tasks in unsupervised machine learning. The goal is to assign unlabeled data to groups, where similar data points hopefully get assigned to the same group. Spectral clustering…

A unification of Maximum Likelihood Estimation and minimizing the sum of squares. — Introduction I recently wrote about maximum likelihood estimation in my ongoing series on the fundamentals of machine learning: Maximum Likelihood Estimation Fundamentals of Machine Learning (Part 2)towardsdatascience.com In that post, we learned what it means to “model” data, and then how to use MLE to find the parameters of our model. In this post, we’re going to dive into linear…

Maximum Likelihood Estimation — This is part two in a series of topics I consider fundamental to machine learning. Part one covered probability theory, which we will build on heavily in this post. If you need to refresh on probability check out part one: Probability — Fundamentals of Machine Learning (Part 1) An overview of the concepts of Probability Theory needed for Machine Learningtowardsdatascience.com

Fundamentals of Machine Learning (Part 1) — This is part one in a series of topics I consider fundamental to machine learning. Probability theory is a mathematical framework for quantifying our uncertainty about the world. It allows us (and our software) to reason effectively in situations where being certain is impossible. Probability theory is at the foundation…