Counting techniques, combinatorial methods, random experiments, sample spaces, events, probability axioms, some rules of probability, conditional probability, independence, Bayes' theorem, random variables (r.v.'s), probability distributions, discrete and continuous r.v.'s, probability density functions, multivariate distributions, marginal and conditional distributions, expected values, moments, conditional expectation, Chebyshev's theorem, product moments, moments of linear combinations of r.v.'s, special discrete distributions, uniform, Bernoulli, binomial, negative binomial, geometric, hypergeometric and Poisson distributions, special probability densities, uniform, gamma, exponential and normal densities, normal approximation to binomial, distribution of functions of r.v.'s, distribution function and moment-generating law of large numbers, the central limit theorem, function techniques, distribution of the mean, basic methods for statistical estimation and hypothesis testing.

- To give an understanding of uncertainty and randomness
- To teach the fundemantal concepts and defintions of probability
- To equip the students with the tools and techniques of probability theory

John Freund’s Mathematical Statistics with Applications, 8th Edition,
Pearson-Prentice Hall, 2014, ISBN 978-1-292-02500-1.