Out-of-Distribution (OOD) Data

Optimization Theory in Artificial Intelligence involves methods and mathematical techniques used to identify the best possible solution from a set of feasible options. It is fundamental in training AI models, as it helps minimize errors or maximize performance by adjusting parameters efficiently, ensuring models learn effectively and achieve optimal results in tasks like prediction, classification, and decision-making.