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.
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.
