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Machine Learning Meetup Notes: 2010-08-18
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====Mike - HMMs==== HMM used for time series data Markov Chains: matrix of transition probabilities: a_i_j is prob to go from state i to j the prob funciton is only a function of the previous state HMM: example is you are trying to predict the weather based on a diary of someone's ice cream eating habits hidden state = what you are trying to predict (weather) obs state: ice cream 3 problems HMMs can solve: Likelihood, Decoding, Training Likelihood: find all the paths Decoding: whats the best sequence of hidden states that produce your obs seq (Viterbi Algorithm), which is similar to Maximum Likelihood Training: Given an obs seq, learn the state transition probs and the emission probs of an HMM (Expectation Maximization, Wells-Baum, Forward-Backward Algorithm)
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