Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
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 chain games, hitting probabilities, and expected hitting times in a target set.
Covers clustering time series data using dynamic time warping, string metrics, and Markov models.
Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.
Explores Markov chains, their properties, and algorithmic applications, emphasizing information quantification and state monotonicity.
