Patterns, predictions, and actions: A story about machine learning

TL;DR

This textbook provides a comprehensive overview of machine learning, covering foundational concepts, datasets, causal inference, decision making, and societal impacts, aimed at readers with basic mathematical background.

Contribution

It offers an integrated narrative connecting core machine learning principles with decision-making and societal considerations, including causal inference and reinforcement learning.

Findings

Historical context of datasets as benchmarks

Introduction to causal inference and decision making

Discussion of societal impacts of machine learning

Abstract

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.