Lecture
This lecture covers regression analysis, constructing larger matrices, multivariate linear regression, principal component analysis, spectral decomposition theorem, principal components transform, and factor models. It delves into finding uncorrelated linear combinations of data, PCA as a data rotation technique, and the aim to account for most data variability.