Linear Algebra: Vector Spaces & Operators

Related lectures (27)

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Explores basis transformation, eigenvalues, and linear operators in inner product spaces, emphasizing their significance in Quantum Mechanics.

Explores the matrix representation of operators and basis transformation in linear algebra.

Explores the application of linear algebra in quantum mechanics, emphasizing vector spaces, Hilbert spaces, and the spectral theorem.

Covers fundamental concepts in quantum mechanics, including vector spaces, superposition, observables, and inner product.

Explores linear operators, boundedness, and vector spaces with a focus on verifying bounded aspects.

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

Offers a crash course on quantum mechanics, covering vector spaces, superposition, observables, and self-adjoint operators.

Explores the application of linear algebra in Quantum Mechanics, emphasizing its importance in understanding materials properties.

Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.

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