Machine Learning

Hidden Markov Models

Probabilistic models for sequences with unobserved underlying states.

A hidden Markov model assumes that an observed sequence was generated by a series of hidden states. Each hidden state has probabilities for producing observations and transitioning to other states.

HMMs have been used for speech recognition, activity detection, finance and biological-sequence analysis. They provide interpretable sequence models but rely on simplifying assumptions about state transitions and observations.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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