Course Name: 

Applied Linear Algebra (IT256)


B.Tech (AI)




Programme Core (PC)

Credits (L-T-P): 

(3-0-2) 4


Vectors: definition, scalars, addition, scalar multiplication, inner product(dot product), vector projection, cosine similarity, orthogonal vectors, normal and orthonormal vectors, vector norm, vector space, linear combination, linear span, linear independence, basis vectors; Matrices: definition, addition, transpose, scalar multiplication, matrix multiplication, hadamard product, functions, linear transformation, determinant, identity matrix, invertible matrix and inverse, rank, trace, popular type of matrices- symmetric, diagonal, orthogonal, orthonormal, positive definite matrix; Least Squares: Least Square Problem and Solutions; Eigen Values; Eigenvectors: Concept, Significance; Principal Component Analysis: Concept, Properties, Applications; Singular Value Decomposition: Concept, Properties, Applications.


W. Cheney, D. Kincaid, “Linear Algebra Theory and Applications”;, Jones & Bartlett, Student Ed.. 2010
Gilbert Strang, “Linear Algebra and Its Applications”, Cengage Learning, 4th Edition, 2007
Stephen Boyd, Lieven Vandenberghe, “An Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares”, Cambridge University Press, 2018.


Information Technology

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Head of the Department,
Department of Information Technology,
National Institute of Technology Karnataka,
SurathkalP. O. Srinivasnagar, Mangalore - 575 025
Ph.:    +91-824-2474056
Email:  hodit [at] nitk [dot] edu [dot] in

Web Admin: Sowmya Kamath S

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