Machine learning is concerned with developing algorithms that learn
from experience, build models of the environment from the acquired
knowledge, and use these models for prediction. Machine learning is
usually taught as a bunch of methods that can solve a bunch of
problems (see my Introduction to SML last week). The following
tutorial takes a step back and asks about the foundations of machine
learning, in particular the (philosophical) problem of inductive inference,
(Bayesian) statistics, and arti¯cial intelligence. The tutorial concentrates
on principled, uni¯ed, and exact methods.
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