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Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.
Introduces hyperbolic geometry, covering complete metric spaces, isometries, and Gaussian curvature in dimension 2.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores finite dimensional spaces, covering extraction process, bases generation, and space completion.
