Master the math behind AI with our BCS405D Linear Algebra VTU Notes. Explore vector spaces, eigenvalues, and SVD decomposition tailored for the 2022 Scheme at the all-new vtubuddy.in student resource portal.
Master the math behind AI with our BCS405D Linear Algebra VTU Notes. Explore vector spaces, eigenvalues, and SVD decomposition tailored for the 2022 Scheme at the all-new vtubuddy.in student resource portal.
Introduction, Vector spaces, Subspaces, Linear Combinations, Linear Spans, row space and column space of a Matrix, Linear Dependence and Independence, Basis and Dimension, Coordinates.
Introduction, Linear Mappings, Geometric linear transformation of i2, Kernel and Image of a linear transformations, Rank-Nullity Theorem (No proof), Matrix representation of linear transformations, Singular and Non-singular linear transformations, Invertible linear transformations
Introduction, Polynomials of Matrices, Applications of Cayley-Hamilton Theorem, Eigen spaces of a linear transformation, Characteristic and Minimal Polynomials of Block Matrices, Jordan Canonical form.
Inner products, inner product spaces, length and orthogonality, orthogonal sets and Bases, projections, Gram-Schmidt process, QR-factorization, least squares problem and least square error
Diagonalization and Orthogonal diagonalization of real symmetric matrices, quadratic forms and its classifications, Hessian Matrix, Method of steepest descent, Singular value decomposition. Dimensionality reduction – Principal component analysis.
BIS654C
BCS654A
BCS613D
BCS613C
BCS3012Mod
BCEDK103
BCSL305
BCS30122550question
BCS303
XYZS301
