math behind machine learning

math behind machine learning algorithms

Vectors

Vectors represent data points in high-dimensional spaces. In ML, we often work with feature vectors that represent objects or observations.

Matrices

Matrices are used to represent datasets, transformations, and model parameters. They enable efficient computation of complex operations.

Eigenvalues and Eigenvectors

These concepts are fundamental to dimensionality reduction techniques like PCA and understanding model behavior.