This course deals with concepts of matrix, determinants, inverse, rank, solution of linear equations, eigen values and eigen vectors in short and crisp manner for GATE preparation and IES. In this section we also explain about linear algebra in machine learning.
Linear algebra is the branch of mathematics concerning linear equations such as.linear functions such as. and their representations in vector spaces and through matrices. Linear algebra is central to almost all areas of mathematics.
Matrices 1 (Basic definitions)
Matrices 2 (Addition of Matrices)
Matrices 3 (Scalar multiplication)
Matrices 4 (Matrix multiplication)
Matrices 5 (Properties of matrix multiplication 1)
Matrices 6 (properties of matrix multiplication 2)
Matrices 7 (Triangular, diagonal and scalar martrices)
Matrices 8 (Idempotent matrices)
Matrices 9 : Involutary matrices
Matrices 10 (Nilpotent matirces)
Matrices 11 (Traspose of a matrix)
Matrices 12 (Symmetric and skew symmetric matrices)
Symmetric and skew symmetric matrices examples 1
Matrices 13 (Hermitian and skew hermitian matrices)
Matrices 14 (Determinant, minor, co factor)
Matrices 15 (Determinants of upper and lower triangular matrices)
Matrices 16 (Theorems on determinant part 1)
Matrices 17 (Theorems on determinant part 2)
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