Course contents: Sample Space and Probability. Sets. Conditional Probability. Total Probability Theorem. Bayes’ Rule. Independence. Counting. Discrete Random Variables. Probability Mass Functions. Functions of Random Variables. Expectation, Mean and Variance. General Random Variables. Cumulative Distribution Functions. Normal Random Variables. Limit Theorems. Markov and Chebyshev Inequalities. The Weak Law and the Strong Law of Large Numbers. The Central Limit Theorem. The Bernoulli and Poisson Processes. Bayesian Statistical Inference. Classical Statistical Inference.

Assessment: Written exams at the end of the semester.