I'll present to you modern solutions to old problems in statistical inference and estimation. This tutorial will introduce new ideas in optimization and bridge the gap between statistical models and your business' problems and scientific problems. What I expect you to learn from this tutorial is:

1. how to code maximum likelihood estimation problems in Python
2. writing (much) better code for numerical optimizations using automatic differentiation
3. how to transform abstract model parameters into business logic, using the delta method
4. avoiding null hypothesis testing and instead focusing on modern interpretations of statistical inference

This will be an optionally hands-on tutorial, so either bring a laptop or sit back and treat it like a lecture!