In this column, we share resources for learning about and teaching Hidden Markov Models (HMMs). HMMs find many important applications in temporal pattern recognition tasks such as speech/handwriting/gesture recognition and robot localization. In such domains, we may have a finite state machine model with known state transition probabilities, state output probabilities, and state outputs, but lack knowledge of the states generating such outputs. HMMs are useful in framing problems where external sequential evidence is used to derive underlying state information (e.g. intended words and gestures). [excerpt]
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Version of Record
Neller, Todd. "AI Education Matters: Teaching Hidden Markov Models." AI Matters: A Newsletter of ACM SIGAI 4, no. 1 (2018): 21-22.
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