Document Type
Article
Publication Date
2018
Department 1
Computer Science
Abstract
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]
Copyright Note
This is the publisher's version of the work. This publication appears in Gettysburg College's institutional repository by permission of the copyright owner for personal use, not for redistribution.
DOI
10.1145/3203247.3203252
Version
Version of Record
Recommended Citation
Neller, Todd. "AI Education Matters: Teaching Hidden Markov Models." AI Matters: A Newsletter of ACM SIGAI 4, no. 1 (2018): 21-22.
Required Publisher's Statement
This blog can also be found on the publisher's website: https://sigai.acm.org/aimatters/
Comments
Additional resources are provided here: http://cs.gettysburg.edu/ai-matters/index.php/Resources