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!