Supervised learning algorithms: Linear and Logistic Regression, Gradient Descent, Support Vector Machines, Kernels, Artificial Neural Networks, Decision Trees, ML and MAP Estimates, K-Nearest…

Problem solving, search techniques, control strategies, game playing (minimax), reasoning, knowledge representation through predicate logic, rule based systems, semantics nets, frames, conceptual…

Linear programs formulation through examples from engineering / business decision making problems, preliminary theory and geometry of linear programs, basic feasible solution, simplex method,…

Introduction to Computers - CPU, ALU, I/O devices,
Introduction to C Programming - Data types, Looping Statements, Arrays, Structure, Functions (Both simple and Recursive function), Call by…

Review of elementary stochastic calculus and Black - Scholes - Merton theory of option pricing. Corporate liabilities and contingent claims. Risk structure of interest rates. Statistical techniques…

Introduction to Graphs: Definition and basic concepts; Trees: characterizations, counting of minimum spanning tree; Paths and Distance in Graphs: Basic Definitions, center and median of a graph,…