DevanshKandpal: Bot: wrap bare course codes in wikilinks

2026-04-14T16:42:16Z

Bot: wrap bare course codes in wikilinks

← Older revision		Revision as of 16:42, 14 April 2026
Line 4:		Line 4:
	\| credits = 3		\| credits = 3
	\| credit_structure = 3-0-0		\| credit_structure = 3-0-0
	\| pre_requisites = MTL101		\| pre_requisites = [[MTL101]]
	\| overlaps = MTL502		\| overlaps = [[MTL502]]
	}}		}}

	== MTL104 : Linear Algebra and Applications ==		== MTL104 : Linear Algebra and Applications ==
	Introduce Fields: fields of numbers, finite fields. Review basis and dimension of a vector space, linear transformations, eigenvalue and eigenvector of an operator. LU Factorization. Some applications giving rise to Linear Systems Problems Dual and double dual of a vector space and transpose of a linear transformation. Diagonalizability of linear operators of finite dimensional vector spaces, simultaneous triangulization and simultaneous diagonalization. The primary decomposition theorem - diagonal and nilpotent parts. Inner product spaces, Gram-Schmidt orthogonalization, best approximation of a vector by a vector belonging a given subspace and application to least square problems. Adjoint of an operator, hermitian, unitary and normal operators. Singular Value Decomposition and its applications. Spectral decomposition. Introduction of bilinear and quadratic forms.		Introduce Fields: fields of numbers, finite fields. Review basis and dimension of a vector space, linear transformations, eigenvalue and eigenvector of an operator. LU Factorization. Some applications giving rise to Linear Systems Problems Dual and double dual of a vector space and transpose of a linear transformation. Diagonalizability of linear operators of finite dimensional vector spaces, simultaneous triangulization and simultaneous diagonalization. The primary decomposition theorem - diagonal and nilpotent parts. Inner product spaces, Gram-Schmidt orthogonalization, best approximation of a vector by a vector belonging a given subspace and application to least square problems. Adjoint of an operator, hermitian, unitary and normal operators. Singular Value Decomposition and its applications. Spectral decomposition. Introduction of bilinear and quadratic forms.

Prashantt492: Creating course page via bot

2026-03-04T10:13:56Z

Creating course page via bot

New page

{{Infobox Course
| code = MTL104
| name = Linear Algebra and Applications
| credits = 3
| credit_structure = 3-0-0
| pre_requisites = MTL101
| overlaps = MTL502
}}

== MTL104 : Linear Algebra and Applications ==
Introduce Fields: fields of numbers, finite fields. Review basis and dimension of a vector space, linear transformations, eigenvalue and eigenvector of an operator. LU Factorization. Some applications giving rise to Linear Systems Problems Dual and double dual of a vector space and transpose of a linear transformation. Diagonalizability of linear operators of finite dimensional vector spaces, simultaneous triangulization and simultaneous diagonalization. The primary decomposition theorem - diagonal and nilpotent parts. Inner product spaces, Gram-Schmidt orthogonalization, best approximation of a vector by a vector belonging a given subspace and application to least square problems. Adjoint of an operator, hermitian, unitary and normal operators. Singular Value Decomposition and its applications. Spectral decomposition. Introduction of bilinear and quadratic forms.

MTL104 - Revision history

DevanshKandpal: Bot: wrap bare course codes in wikilinks

Prashantt492: Creating course page via bot