500

Differentiation and integration of functions; basic matrix operations; linearization; linear and nonlinear optimization techniques; clustering and similarity measures, introduction to probability and statistics, basic computational algorithms. Includes frequent illustration of concepts using mathematical computation tools.

Distribution of random variables, conditional probability and independence, distributions of functions of random variables, limiting distributions.

Point estimation, sufficient statistics, completeness, exponential family, maximum likelihood estimators, statistical hypotheses, beta tests, likelihood ratio tests, noncentral distributions.