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This lecture covers the definitions and topologies of Hidden Markov Models (HMM). It explains the concept of observable data, the learning process of HMM, ergodic topology, and the application of HMM in speech and motion prediction. The lecture also discusses the computational costs, complexity of data, and the current research trends in speeding up the estimation of variables in HMM.
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