Subscribe to DSC Newsletter

Summary:  At the core of modern AI, particularly robotics, and sequential tasks is Reinforcement Learning.  Although RL has been around for many years it has become the third leg of the Machine Learning stool and increasingly important for Data Scientist to know when and how to implement.

 

If you poled a group of data scientist just a few years back about how many machine learning problem types there are you would almost certainly have gotten a binary response:  problem types were clearly divided into supervised and unsupervised.

  • Supervised: You’ve got labeled data (clearly defined examples).
  • Unsupervised: You’ve got data but it’s not labeled.  See if there’s a structure in there.

Today if you asked that same question you are very likely to find that machine learning problem types are divided into three categories:

 

While Reinforcement Learning (RL) has been around since at least the 80’s and before that in the behavioral sciences, its introduction as a major player in machine learning reflects it rising importance in AI.

Click here to read full article. 

Views: 63

Reply to This

On Data Science Central

© 2019   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service