Lectures related to Modern Regression: Maximum Likelihood Estimation

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Explores Generalized Additive Models, covering basics, smooth functions, penalties, practical examples in R, and linear mixed models.

Explores orthogonal vectors in a vector subspace and their dimension.

Explores the Kernel-Image Theorem, bijectivity conditions, linear forms, and surjectivity in linear algebra.