Signal Representations

Related lectures (28)

Page 2 of 3

Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.

Explains linear independence, bases, and dimension in vector spaces, including the importance of the order of vectors in a basis.

Covers the principles of quantum physics, focusing on tensor product spaces and entangled vectors.

Explores weak derivatives in Sobolev spaces, discussing their properties and uniqueness.

Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

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

Introduces function spaces and Hilbert spaces, discussing inner product spaces and the importance of completeness in Hilbert spaces.

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

Covers Dirac's notation in linear algebra and the tensor product concept in Hilbert spaces.

Introduces orthogonality between vectors, angles, and orthogonal complement properties in vector spaces.