Advanced course in machine learning methods.
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frequentist vs bayesian approach in statistical modeling
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bayesian networks (probabilistic graphical models)
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parameter inference in probabilistic graphical models with fully observed data
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EM algorithm (parameter estimation in models with hidden variables)
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Markov chains and Hidden Markov <odels, as examples of bayesian networks, parameter estimation and inference
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Exact inference in graphical models (factor graphs, the sum product algorithm, Cluster trees, potentials, Message passing, Junction tree algorithm)
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model selection, model evidence, learning model structure, tree models, general models, structural EM
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Sampling (MCMC, Gibbs sampling)
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variational inference.
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exploratory data analysis on example of single cell RNA seq data