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Adam Green

Adam Green is an Energy Data Scientist at the startup Tempus Energy. He is on a mission to decarbonise the supply of heat and electricity, and in particular to use machine learning in solving energy problems. He forecasts energy prices using supervised Neural Networks, and he is testing Reinforcement Learning models to optimize energy systems.
Adam holds a Masters of Science in Advanced Process Design for Energy from the University of Manchester, UK. He is an alumnus of Data Science Retreat (Batch 09).
https://www.linkedin.com/in/adgefficiency/
www.adgefficiency.com
 

Introduction to reinforcement learning

LEARNING OUTCOMES

  • understand what makes reinforcement learning different from supervised or unsupervised learning
  • understand some of the challenges in reinforcement learning
  • understand the strengths & weaknesses of some of the different approaches to reinforcement learning

Reinforcement learning is one of the most exciting fields in machine learning. It's a method where we allow machines to learn through their own actions.

Course outline

  • Introduce reinforcement learning in the context of the wider machine learning space
  • Introduce key concepts & challenges in reinforcement learning
  • Introduce some famous reinforcement learning algorithms (Monte Carlo, Temporal Difference, Q-Learning, Policy Graidents)
  • Practical session where we develop a model to play with Open AI gym

Prerequisites:

  • Basic understanding of Python