Reinforcement Learning with Databricks

Reinforcement Learning with Databricks

Summary

In this course, students learn about Reinforcement Learning (RL) paradigm and get hands-on experience on developing/coding RL algorithms.

Description

Reinforcement Learning (RL) is an area of machine learning concerned with agents (algorithms) taking actions in an environment in order to maximize some notion of cumulative reward. RL is the one of three machine learning paradigms, alongside supervised learning and unsupervised learning. In this course, students will learn what Reinforcement Learning is, how to formulate such a problem and how to implement different RL algorithms.

Duration

2 Days

Objectives

Upon completion of the course, students should be able to:

 

  • Formulate a Reinforcement Learning problem

  • Understand the difference between Supervised, Unsupervised, and Reinforcement Learning

  • Understand Markov Decision Processes (MDPs)

  • Understand Dynamic programming

  • Understand and implement Policy evaluation, Policy Iteration, and Value Iteration algorithms

  • Understand and implement Model-Free Prediction and Control algorithms such as first-visit Monte Carlo, Temporal Difference and SARSA.

Audience

  • Data scientist

  • Quantitative Researcher

     

Prerequisites

  • Intermediate experience with Python programming
  • Intermediate experience with Numpy and Pandas
  • Intermediate experience with Machine Learning
  • Intermediate level knowledge of Probability Theory
  • Intermediate level knowledge of Linear Algebra

 

 

Additional Notes

  • The appropriate, web-based programming environment will be provided to students
  • This class is taught in Python only

Upcoming Classes

No classes have been scheduled, but you can always Request a Class.