Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.
Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.
Introduces risk analysis methods like PHA, Checklist, and FMECA, emphasizing systematic evaluation and failure analysis.
