Machine learning is usually taught as a bunch of methods that can solve a bunch of problems (see above). The second part of the tutorial takes a step back and asks about the foundations of machine learning, in particular the (philosophical) problem of inductive inference, (Bayesian) statistics, and artificial intelligence. It concentrates on principled, unified, and exact methods.