Applications include the 0-1 laws of Kolmogorov and Hewitt-Savage, the strong laws for U-statistics and exchangeable sequences, De Finetti's theorem for exchangeable sequences and Kakutani's theorem for product martingales. A simple central limit theorem for martingales is proven and applied to a basic urn model, the trace of a random matrix and Markov chains. Additional topics include forward martingale representation for U-statistics, conditional Borel-Cantelli lemma, Azuma-Hoeffding inequality, conditional three series theorem, strong law for martingales and the Kesten-Stigum theorem for a simple branching process. The prerequisite for this course is a first course in measure theoretic probability. The book recollects its essential concepts and results, mostly without proof, but full details have been provided for the Radon-Nikodym theorem and the concept of conditional expectation.
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