Mathematical Foundations for Machine Learning

Explain the intuition of a derivative as a rate of change and its use in finding minima/maxima of functions.

Describe the concept of a gradient as a vector of partial derivatives for multi-variable functions.

Apply basic vector and matrix operations, including dot products and matrix-vector multiplication.

Explain the role of eigenvalues and eigenvectors in representing the principal axes of data variation.

Relate the chain rule from calculus to the concept of backpropagation in neural networks.

Distinguish between convex and non-convex functions and explain the implications for optimization.