  This graduate textbook on machine learning tells a story of how patterns in
data support predictions and consequential actions. Starting with the
foundations of decision making, we cover representation, optimization, and
generalization as the constituents of supervised learning. A chapter on
datasets as benchmarks examines their histories and scientific bases.
Self-contained introductions to causality, the practice of causal inference,
sequential decision making, and reinforcement learning equip the reader with
concepts and tools to reason about actions and their consequences. Throughout,
the text discusses historical context and societal impact. We invite readers
from all backgrounds; some experience with probability, calculus, and linear
algebra suffices.
