MATH-111(c): Linear Algebra

Lectures in this course (92)

Explores the theory and applications of matrix determinants, emphasizing their role in matrix invertibility and solution uniqueness.

Covers the properties of matrix determinants and strategies for their calculation.

Explores the diagonalization of symmetric matrices and the importance of Singular Value Decomposition.

Covers matrix kernels, images, linear applications, independence, and bases in vector spaces.

Explains coordinate application in vector spaces and the uniqueness of vector components in different bases.

Explores changing bases in vector spaces using matrices and the significance of preserving structure within vector spaces.

Explores changing bases in vector spaces and matrix representation of linear transformations.

Explores matrix representation of linear applications, emphasizing eigenvalues and bases.

Covers matrix operations, scalar products, vector norms, and orthogonality in vector spaces.

Explores diagonalization in symmetric matrices, emphasizing orthogonality and orthonormal bases.

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.

Covers the fundamentals of linear equations systems, including definitions, representations, and solution methods.

Delves into linear equations, vector spaces, and matrix operations, exploring solutions and representations through examples and algebraic methods.

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