In this lecture, we will look at probabilistic criteria for defining what it means to learn. Specifically, we will see maximum a posteriori and maximum likelihood learning criteria with examples.
Links and Resources
Chapter 6 of Tom Mitchell’s book
Chapter 7 of A Course in Machine Learning by Hal Daume
Chapters 2 and 3 of Pattern Classification by Duda, Hart and Stork