I'm reading this for fun -- tutorials and book recommendations on the Bayesian methods toolboox with a cognitive science/machine learning slant. Comes from the Computational Cognitive Science Lab at Berkeley. I recommend the general 2008 tutorial.
Useful stuff included in tutorial:
Parameter estimation
Model selection
Why Occam's Razor emerges naturally from the Conservation of Expected Evidence
Graphical models
Hierarchical Bayesian models
I like this source for Bayesian nonparametrics; the disadvantage is that it's mostly scribe notes, but a lot of the referenced papers are well-written and explain important material.
EDIT: This was supposed to be a reply to Cyan's post.