LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]

04/28/15 • 39 min

In this episode of Learning Machines 101 we discuss the design of statistical learning machines which can make inferences about rare and unseen events using prior knowledge. Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

Previous Episode

LM101-026: How to Learn Statistical Regularities (Rerun)

In this rerun of Episode 10, we discuss fundamental principles of learning in statistical environments including the design of learning machines that can use prior knowledge to facilitate and guide the learning of statistical regularities. The topics of ML (Maximum Likelihood) and MAP (Maximum A Posteriori) estimation are discussed in the context of the nature versus nature problem.

Check out: www.learningmachines101.com to obtain transcripts of this podcastand access to free machine learning software!

Next Episode

LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]

This rerun of an earlier episode of Learning Machines 101 discusses the problem of how to evaluate the ability of a learning machine to make generalizations and construct abstractions given the learning machine is provided a finite limited collection of experiences. Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!