Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
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.
Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
