Machine learning

Machine learning (ML) is the branch of AI where a system learns patterns from data rather than following rules a programmer wrote by hand. Instead of coding the logic directly, you define a model with adjustable parameters and an objective (a loss function), then run training: feed in examples, measure how wrong the predictions are, and nudge the parameters to reduce that error — repeated over and over until the model generalizes. Once trained, using it on new inputs is inference. The classic split is by what the data looks like: supervised learning trains on labeled examples (input → correct output), unsupervised learning finds structure in unlabeled data (clustering, latent space embeddings), and reinforcement learning trains an agent via rewards from acting in an environment. Deep learning is the modern subfield that uses many-layered neural networks built from tensor operations — the transformer and GAN are deep-learning architectures — and it’s what powers most of what people now call “AI.” The perennial hazard across all of it is generalization: a model that aces training data but flops on new data has overfit, which is why you watch validation loss.