Reinforcement Learning

Get a crash course in this key technique for machine learning.


Value Functions

Dynamic Programming

Monte Carlo

Temporal Difference Learning

Policy Gradient Methods

Course description

This course was written by Tessa van der Heiden, a researcher and developer of autonomous driving algorithms at BMW. In this course, you'll learn the mathematical underpinnings of reinforcement learning, a foundational machine learning technique in which an agent (or algorithm) is trained by trial and error. By rewarding the agent for good outcomes, it "learns" optimal strategies, which can be applied to problems in domains like robotics, quantitative trading, and game theory. This course is intended for young professionals who are interested in applying machine learning techniques for decision making, or students who are pursuing a machine learning career or preparing for interviews.


  • Introduction to Neural Networks