Linear Algebra
Cover matrix fundamentals, subspaces, projections, spectral structure, and decomposition.
Study the calculus, linear algebra, probability, and statistics used in machine learning.
Cover matrix fundamentals, subspaces, projections, spectral structure, and decomposition.
Cover single-variable foundations, multivariable differentiation, approximation, and optimization.
Move from events and conditioning through moments, distributions, and asymptotic results.
Cover estimators, likelihood, Bayesian updating, intervals, tests, and simple regression.