Attacking Machine Learning with Adversarial Examples

Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across differen… Read more

Similar

Notes on Causality in Machine Learning

I try to consolidate my MLSS 2020 notes in small blog posts and hope you might also find them interesting. I don’t try to cover the complete lectures but rather pick some pieces that I find important when working or doing research in ML. I anticipate this... (more…)

Read more »

Algorithmic Aspects of Machine Learning

This course is organized around algorithmic issues that arise in machine learning. Modern machine learning systems are often built on top of algorithms that do not have provable guarantees, and it is the subject of debate when and why they work. In this c...

Read more »