This class was tough.
Course Overview:
This course is a very important one as it has diverse applications in all branches of engineering. So, it should be taken seriously with dedication.
Course highlights:
Vectors in Rn , notion of linear independence and dependence, linear span of a set of vectors, vector sub spaces of Rn , basis of a vector subspace • Systems of linear equations, matrices and Gauss elimination, row space, null space, and column space, rank of a matrix. • Determinants and rank of a matrix in terms of determinants. • Abstract vector spaces, linear transformations, matrix of a linear transformation, change of basis and similarity, rank-nullity theorem. • Inner product spaces, Gram-Schmidt process, orthonormal bases, projections and least squares approximation. • Eigenvalues and eigenvectors, characteristic polynomials, eigenvalues of special matrices ( orthogonal, unitary, hermitian, symmetric, skew symmetric, normal). algebraic and geometric multiplicity, diagonalization by similarity transformations, spectral theorem for real symmetric matrices, application to quadratic forms.
Hours per week:
9-11 hours
Advice for students:
Take the course seriously. Proper dedication can secure good grades in the course. Grading is competitive in the course.