Vector Spaces: Basics

Related lectures (29)

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Covers linear applications between vector spaces, exploring their properties and uniqueness based on bases.

Covers the definitions and properties of vector spaces, including axioms and examples.

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.

Explores vector space properties, operations, linear combinations, and subspaces construction.

Explores the properties and applications of the dot product in vector spaces.

Introduces linear combinations of vectors in R^n and their properties.

Introduces the concept of vector spaces and covers properties of vectorial subspaces.

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

Explores orthogonality between vectors and subspaces, demonstrating practical implications in matrix operations.

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