MAML (Model-Agnostic Meta-Learning) finds weight initialization enabling rapid adaptation to new tasks with gradient descent. Core idea: Learn θ such that few gradient steps on new task produce good task-specific parameters. Not learning final weights, but learning where to start. Algorithm: For each training task: compute adapted params θ' = θ - α∇L_task(θ), evaluate loss on query set with θ', update θ using gradient through adaptation (second-order). Key insight: Optimize for post-adaptation performance, not initial performance. Learns initialization sensitive to task-specific gradients. First vs second order: Full MAML uses Hessian (expensive), First-Order MAML (FOMAML) approximates (much cheaper, often works well), Reptile (even simpler approximation). Model-agnostic: Works with any differentiable model - vision, NLP, RL. Challenges: Computational cost (nested loops, second derivatives), requires many tasks for training, sensitive to hyperparameters. Applications: Few-shot image classification, robotic skill learning, personalized recommendations, fast NLP adaptation. Foundational meta-learning algorithm still widely used and extended.