Machine learning

Machine learning (ML) is a subfield of artificial intelligence focused on developing statistical algorithms that learn from data and improve their performance through experience without explicit programming. While traditional computing follows rigid rules, ML uses data-driven methodologies to extract meaningful patterns and generalize them to unseen information.

The framework of machine learning is built upon three primary pillars:

Data: Information is typically converted into a numerical format and represented as vectors.

Models: These are mathematical functions or probability distributions that describe the process of generating the data or mapping inputs to outputs.

Learning: This is the process of automatically finding patterns by optimizing model parameters so the model performs better on new tasks.

Machine learning relies heavily on several mathematical disciplines, including linear algebra, vector calculus, probability, and optimization. A major challenge in the field is overfitting, where a model becomes so flexible that it "memorizes" training data but fails to generalize to new data. Techniques like regularization are used to discourage overly complex solutions and improve generalization. Additionally, neural scaling laws govern how model performance improves as model size (N), data size (D), and compute (C) increase.