👉 Learned math, often referred to as machine learning or artificial intelligence, involves algorithms that enable computers to learn from and make predictions or decisions based on data without being explicitly programmed. At its core, learned math combines statistical theory and computational methods to uncover patterns and relationships within data. For instance, regression techniques help predict continuous outcomes by fitting a model to observed data, while classification algorithms categorize inputs into discrete classes. Neural networks, inspired by the human brain, use layers of interconnected nodes to learn complex mappings from inputs to outputs through backpropagation and optimization techniques like gradient descent. These methods allow systems to improve their performance over time, adapting to new data and refining their predictions or classifications, effectively mimicking human learning processes in a computational context.
