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Machine learning (ML) is a branch of artificial intelligence focused on building statistical algorithms that learn patterns from data and generalize to new, unseen examples without being explicitly programmed for each task. Its foundations draw heavily on statistics and mathematical optimization, and many learning methods can be framed as minimizing a loss function (often described as empirical risk minimization). Deep learning—using multi-layer neural networks—has been especially influential in achieving high performance across many domains. The history of ML traces back to early ideas about learning and neural computation. The term “machine learning” was coined in 1959 by Arthur Samuel, and early programs in the 1950s explored learning-like behavior (e.g., improving performance in checkers). In parallel, theoretical work on neural mechanisms—such as Donald Hebb’s 1949 ideas about how neuron interactions could shape learning—helped inspire later algorithmic approaches. Researchers including Walter Pitts and Warren McCulloch contributed early mathematical models of neural networks, and by the early 1960s systems like Raytheon’s “Cybertron” demonstrated experimental learning machines using rudimentary reinforcement learning. As the field matured, formal definitions and milestones helped shape what ML became. Tom M. Mitchell offered a widely cited operational definition of learning based on improving performance with experience. Interest in neural networks continued through periods of renewed focus, including the reinvention of backpropagation in the mid-1980s, and ML re-emerged as a distinct, flourishing field in the 1990s by shifting toward statistical and probabilistic methods rather than purely symbolic AI. Later breakthroughs included generative adversarial networks (GANs) in 2014 and reinforcement-learning systems such as AlphaGo, which won against top human players by 2016.
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