Here’s an interesting session from the SciPy 2020 virtual conference.

As a foundational tutorial in statistics and Bayesian inference, the intended audience is Pythonistas who are interested in gaining a foundational knowledge of probability theory and the basics of parameter estimation. Knowledge of `numpy`, `matplotlib`, and Python are prerequisites for this tutorial, in addition to curiosity and an excitement to learn new things!

In this video, Siraj Raval explores how key reinforcement learning algorithms help explain how the human brain works, specifically through the lens of the neurotransmitter known as ‘dopamine’.

These algorithms have been used to help train everything from autopilot systems for airplanes, to video game bots. TD-Learning, Rescorla-Wagner, Kalman Filters, and Bayesian Learning, all in one go!