Graded vector space

Related lectures (29)

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Explains bases and dimension in vector spaces, covering generative and free families, isomorphism, and key theorems.

Covers the standard properties of distance and norm in vector spaces.

Covers kernels and images of linear transformations between vector spaces, illustrating properties and providing proofs.

Explains kernel, image, and linear maps, illustrating concepts with examples.

Covers the definition of vector spaces, subspaces, and linear combinations of vectors.

Introduces linear combinations in vector spaces, operations, and polynomials of degree 2.

Covers the representation of signals in vector spaces and inner product spaces, including the Projection Theorem.

Explores injective and surjective linear applications, map composition, and matrix relationships in vector spaces.

Explores vector subspaces in R4, symmetric matrices, basis vectors, and canonical forms.