3. Syllabus (1 of 3)
Introduction to vectors (definition, types, representations);linear combinations; vector algebra,
operations (dot products, cross products); Projection; 1D and 2D motion
Vector Geometry - lines (Parametric and normal form); planes (parametric and normal forms)
Matrices; rank; minor; System of linear equations - augmented matrix; row echelon form;
Gauss Elimination; pivoting; Reduced row echelon form; Gauss Jordan Elimination
Homogeneous equations; Span; linear independence
Matrix algebra; addition and scalar multiplication, matrix multiplication; rank of a matrix,
determinants-I,
4. Syllabus (2 of 3)
Inverse; invertibility; LU factorization
vector spaces-I; linear dependence of basis;
subspaces; dimension, rank and nullity; rank-nullity theorem
Linear transformations (maps), domains; codomains; image; range and kernel of a linear map;
composition of linear maps; Inverse of a linear transformation;
Matrix associated with a linear map. Linear Transformations: Rotations, Reflections, Scaling,
Shearing, Projection.
Homogeneous coordinates; Affine transformations; composite transformations; change of
coordinates
Eigenvalues, eigenvectors,
Cramer’s rule; Determinants-II; similarity and diagonalization;
orthogonality; orthogonal/orthonormal basis, matrices, projections, decomposition;
5. Syllabus (3 of 3)
Gram-Schmidt orthogonalization;
Spectral theory; QR decomposition;
Orthogonal diagonalization;
quadratic forms; principal axis theorem; constrained optimization;
Principle Component Analysis
linear vector spaces-II; change of bases;
Inner product spaces; norm and distance;
least squares approximation,
Introduction to neural networks; Covariance matrix;
Tensor; Hadamard product;
Pseudoinverse; convolution
Linear regression
Revision and make up
Working with system of linear equations
Explain what is linear equation
Give example 2-3 variable equation, solve it by back substitution
How can we solve 10 million equations like this
Relevant data occupies only a small portion of the ambient large-dimensional vector space
Working within that smaller space increases efficiency, saves resources, and reveals relevant structure in the data
Matrix transformation is used for CG (axis rotation / flipping etc)
GPS systems (x,y,z & t) – 4 satellites