Review of basic vector and matrix operations, Orthogonality, Projection, Eigendecomposition, Factorizations, Covariance, Multivariate Gaussian, Minimum Problems, Lagrange Multipliers, Linear Programming, Least-Square Estimation, Maximum Likelihood Estimation, Gradient Descent. Applications to Machine Learning using Linear Regression and Neural Networks.

Pre-requisites: (MATH102 Or MATH106) And (STAT201 Or STAT212 Or STAT319 Or ISE205) And (ICS103 Or ICS104)