← GATE DA guide GATE DA · Section 2
Linear Algebra
Vector spaces, eigenvalues and the matrix decompositions used in data science — directly covered.
Books that cover Section 2
Looking for the right book for this section? These are the titles from our RGPV AI & Data Science range that cover it — tap through for full contents, price and buy options.
Introduction to Discrete Structure & Linear Algebra
B.Tech AI & Machine Learning, Semester 4 · Dr. D.C. Agarwal, Dr. Pradeep K. Joshi
Covers: The AI-oriented linear algebra of this section — determinant and trace, eigenvalue decomposition and singular value decomposition (SVD), and vector-space structure.
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Engineering Mathematics-I
B.Tech Semester 1 · Dr. D.C. Agarwal
Covers: Vector spaces, basis and linear independence, and matrices — rank, eigenvalues and eigenvectors, diagonalisation and the Cayley-Hamilton theorem.
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Statistical Mathematics (MCA)
MCA 1st Semester · Dr. D.C. Agarwal, Dr. Pradeep K. Joshi
Covers: Matrices and eigenvalue problems — rank, systems of linear equations, eigenvalues and eigenvectors, Cayley-Hamilton and the matrix inverse.
View details Full syllabus — Section 2
- Vector space, subspaces, linear dependence and independence of vectors
- Matrices — projection matrix, orthogonal matrix, idempotent matrix, partition matrix and their properties; quadratic forms
- Systems of linear equations and solutions; Gaussian elimination; eigenvalues and eigenvectors; determinant, rank, nullity; projections
- LU decomposition; singular value decomposition (SVD)