Machine learning methods learn patterns from data instead of expressing every rule explicitly. Core forms include supervised, unsupervised, self-supervised and reinforcement learning.
Engineers need a working grasp of data splits, loss functions, metrics, generalisation, bias-variance reasoning and error analysis. In this knowledge base, machine learning is treated as engineering material: useful only when its behaviour can be measured against real tasks and connected to software constraints.
