LM101-086: Ch8: How to Learn the Probability of Infinitely Many

LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes

Listen now

Description

This 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of observed outcomes in a space-time continuum which corresponds to our physical world. The machine learning algorithm uses information about the frequency of environmental events to support learning. Along the way we discuss measure theory mathematical tools such as sigma fields, and the Radon-Nikodym probability density function as well as the intriguing Banach-Tarski paradox.

More Episodes

See all »

Learning Machines 101

Published 07/20/21

LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges

This 85th episode of Learning Machines 101 discusses formal convergence guarantees for a broad class of machine learning algorithms designed to minimize smooth non-convex objective functions using batch learning methods. Simple mathematical formulas are presented based upon research from the late...

Published 05/21/21

LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems

In this episode of Learning Machines 101, we review Chapter 6 of my book “Statistical Machine Learning” which introduces methods for analyzing the behavior of machine inference algorithms and machine learning algorithms as dynamical systems. We show that when dynamical systems can be viewed as...

Published 01/05/21