General Linear Model: Symmetric Matrices

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Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Explores the diagonalization of symmetric matrices and the orthogonality of eigenvectors.

Explores the diagonalization of symmetric matrices through orthogonal decomposition and the spectral theorem.

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Explores the spectral decomposition of symmetric matrices, including diagonalization and orthogonal basis change matrices.

Covers fundamental linear algebra concepts like vector spaces and eigenvalues.

Introduces regularization techniques to prevent overfitting in machine learning models.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.