6883e621_ws___Machine_Learning__Theory___3
[0] Bayesian Learning [1] You specify a prior probability distribution over data-makers, P(datamaker) then use Bayes law to find a posterior P(datamaker|x). True Bayesians integrate over the posterior to make predictions while many simply use the world with largest posterior directly. [2] Handles the small data limit. Very flexible. Interpolates to engineering. [3] 
Information theoretically problematic. Explicitly specifying a reasonable prior is often hard. Computationally difficult problems are commonly encountered. Human intensive. Partly due to the difficulties above and partly because “first specify a prior” is built into framework this approach is not very automatable.
All Models of Learning have Flaws « Machine Learning (Theory)
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