👉 Buried math refers to the underlying mathematical principles and structures that often remain implicit or unstated but are crucial for understanding and solving complex problems. For instance, in machine learning, the mathematical foundation includes linear algebra (for representing data as vectors and transformations), calculus (for optimizing functions like loss), probability theory (for modeling uncertainty), and statistics (for making inferences). Even in seemingly simple algorithms, concepts like combinatorics (for counting possibilities) and graph theory (for network analysis) are often at play. The beauty lies in how these abstract mathematical ideas seamlessly integrate to create powerful tools for modeling and solving real-world challenges, making the "buried math" both profound and pervasive in modern technology and science.
