Parts and Vectors in Coordinates

Related lectures (29)

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Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Covers translations, homotheties, and geometric transformations using vectors and matrices.

Explores orthogonality, dot product properties, vector norms, and angle definitions in vector spaces.

Covers linear transformations, matrices, kernels, and properties of invertible matrices.

Explores projections on lines and symmetries in 2D space, emphasizing fixed points and symmetric matrices.

Covers the theorems related to orthogonal projection and orthonormal bases.

Covers the properties of vectors, including commutativity, distributivity, and linearity.

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Explores vector and normal equations, covering lines, intersections, and distances in 2D and 3D geometry.

Covers 3D orientation, cross product, and volume calculations using mixed product in vector calculus.