- [[machine learning]], [[machine-learning algorithms]] - [[statistical-computational-information-theoretic laws that govern all learning systems]] - [[gradient-based optimization algorithms]] - [[neural networks]] - [[topic modeling]] - [[reinforcement learning]] - [[causal inference]] - [[environments (machine learning and reinforcement learning)]] - [[recommendation system]] - [[ethics of machine learning]] # Idea [[machine learning|Machine learning]] is a discipline that aims to develop computers that can learn automatically from experience and identify laws that govern learning systems. [[machine-learning algorithms]] search through different models—guided by training experience—to identify a model or program that optimizes performance. # References ## Quotes > p256. As a field of study, machine learning sits at the crossroads of computer science, statistics and a variety of other disciplines concerned with **automatic improvement over time**, and **inference and decision-making under uncertainty**. Related disciplines include the **psychological study of human learning**, the **study of evolution**, **adaptive control theory**, the study of educational practices, neuroscience, organizational behavior, and economics. > p257. We appear to be at the beginning of a decades-long trend toward increasingly data-intensive, evidence-based decision making across many aspects of science, commerce, and government.