Numerical Methods:
Significant figures, Error definitions, Approximations and round off errors, accuracy and precision. Roots of Equations: Bairstow-Lin’s Method, Graeffe’s Root Squaring Method. Computation of eigen values of real symmetric matrices: Jacobi and Givens method.
Statistical Inference:
Introduction to multivariate statistical models: Correlation and Regression analysis, Curve fitting (Linear and Non linear)
Probability Theory:
Probability mass function (p.m.f), density function (p.d.f), Random variable: discrete and continuous, Mathematical expectation, Sampling theory: testing of hypothesis by t-test and chi - square distribution.
Graph Theory:
Isomorphism, Planar graphs, graph coloring, Hamilton circuits and Euler cycle. Specialized techniques to solve combinatorial enumeration problems.
Vector Spaces:
Vector spaces; subspaces; Linearly independent and dependent vectors ; Bases and dimension; coordinate vectors-Illustrative examples. Linear transformations; Representation of transformations by matrices; linear functional; Non singular Linear transformations; inverse of a linear transformation- Problems.