Meta-learning trains models to quickly adapt to new tasks with minimal examples - "learning to learn." Goal: Learn general adaptation strategy across many tasks, apply to new tasks with few examples. Problem setup: Training involves many tasks (each with support/query sets), model learns what transfers across tasks, evaluated on ability to adapt to held-out tasks. Key approaches: Metric-based: Learn embedding space where similar examples cluster (Prototypical Networks, Matching Networks). Optimization-based: Learn initialization for fast adaptation (MAML). Model-based: Learn model that directly produces new model weights or predictions. Training: Sample task → fine-tune on support set → evaluate on query set → update meta-parameters based on performance. Few-shot classification setup: N-way K-shot - classify among N classes with K examples each. Applications: Robotics (new skills quickly), drug discovery, personalization, low-resource languages. Challenges: Task distribution matters, computational cost, transferring to very different tasks. Foundation for few-shot learning research.