Lectures related to PCA: Intuition

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Covers the reduction of a linear application and finding corresponding reduced forms and bases.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores matrix operations, linear systems, solutions, and the span of vectors in linear algebra.

Covers calculations and algorithms for determining the determinant of a matrix, including permuting rows and multiplying by coefficients.

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

Explores linear applications, matrices, injectivity, unique solutions, and matrix operations.