PCA: Principal Components Analysis

Related lectures (26)

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Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.

Covers the goals and motivations of representation theory, focusing on associative algebras and homomorphisms.

Introduces the Simplex Method for optimal decision making in linear programming, covering basic and advanced concepts.

Explores Kernel Principal Component Analysis, a nonlinear method using kernels for linear problem solving and dimensionality reduction.

Explains linear independence, basis, and matrix rank with examples and exercises.

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Explains how to calculate the dimension of a kernel of a matrix transpose.

Explores matrix rank, subspaces, and their role in solving linear systems of equations.

Covers Quantum Electrodynamics (QED), instantons, Feynman rules, and gauge theories in modern particle physics.

Covers linear systems in continuous time, stability analysis, and mass-spring system examples.