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Various topics in linear algebra that are relevant for machine learning, including bases, span, orthogonal and orthonormal bases, orthogonal and orthonormal matrices, determinants, eigenvectors, and principal component analysis (pca). It also discusses singular and non-singular transformations, the rank of linear transformations, and systems of linear equations. A comprehensive overview of these fundamental linear algebra concepts and their applications in the field of machine learning. It could be useful for students, researchers, or professionals working in machine learning or related areas who need to understand the mathematical foundations underlying these techniques.
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Linear algebra - Week 4
PCA
Principal Component Analysis
Principal Component Analysis
Principal Component Analysis
Principal Component Analysis
Principal Component Analysis
Principal Component Analysis
2 dimensions 1 dimension
Principal Component Analysis
Principal Component Analysis
8 dimensions
Principal Component Analysis
8 dimensions 3 dimensions
Non-singular transformation
a
b
Non-singular transformation
a
b
