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.
Covers quantum entropy, density matrices, and entanglement in quantum information theory.
Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.
Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.
