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Lecture 9: Hidden Markov Models

Lecture 9: Hidden Markov Models Working with time series data Hidden Markov Models Inference and learning problems Forward-backward algorithm Baum-Welch algorithm for parameter fittingCOMP-652 and ECSE-608, Lecture 9 - February 9, 20161 Time series/sequence data Very important in practice: Speech recognition Text processing (taking into account the sequence of words) DNA analysis Heart-rate monitoring Financial market forecasting Mobile robot sensor processing .. Does this fit the machine learning paradigm as described so far? The sequences arenot all the same length(so we cannot just assumeone attribute per time step) The data at each time slice/index isnot independent The data distributionmay change over timeCOMP-652 and ECSE-608, Lecture 9 - February 9, 20162 Example: Robot position tracking1 Illustrative Example: Robot Localization!

Hidden Markov Models (HMMs) Hidden Markov Models (HMMs) are used for situations in which: { The data consists of a sequence of observations { The observations depend (probabilistically) on the internal state of a dynamical system { The true state of the system is unknown (i.e., it is a hidden or latent variable) There are numerous applications ...

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