Statistical machine learning has made a rapid progress in the past decade and revolutionized our society. It also has rich philosophical implications, in particular with respect to the problem(s) of induction, epistemological justification, causality, explanation and understanding, possible worlds, and natural kinds, to name a few. In this lecture we focus on two major developments in the recent machine learning literature, deep learning and causal modeling, and explore their philosophical (to be distinguished from ethical) issues and implications.