Probability - Math for Machine Learning

In this video, W&B's Deep Learning Educator Charles Frye covers the core ideas from probability that you need in order to do machine learning. In particular, we'll see why mathematically rigorous probability theory is so challenging, and then go over why negative logarithms of probabilities, aka "surprises", show up so often in machine learning. Slides here: http://wandb.me/m4ml-probability Exercise notebooks here: https://github.com/wandb/edu/tree/mai... Check out the other Math4ML videos here: http://wandb.me/m4ml-videos 0:00 Introduction 1:45 Probability is subtle 7:45 Overview of takeaways 8:47 Probability is like mass 17:51 Surprises show up more often in ML 21:46 Surprises give rise to loss functions 24:31 Surprises are better than densities 33:35 Gaussians unite probability and linear algebra 39:57 Summary of the Math4ML ideas 41:40 Additional resources on Math4ML