Patterns, predictions, and actions: A story about machine learning
It serves as an educational resource for students and practitioners, offering a broad overview without presenting new research findings.
This graduate textbook provides a comprehensive introduction to machine learning, covering foundational concepts like decision making, supervised learning, causality, and reinforcement learning, while emphasizing historical context and societal impact.
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.