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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Ai Education Matters: Teaching Hidden Markov Models, Todd W. Neller
Ai Education Matters: Teaching Hidden Markov Models, Todd W. Neller
Computer Science Faculty Publications
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]
Ai Education Matters: Lessons From A Kaggle Click-Through Rate Prediction Competition, Todd W. Neller
Ai Education Matters: Lessons From A Kaggle Click-Through Rate Prediction Competition, Todd W. Neller
Computer Science Faculty Publications
In this column, we will look at a particular Kaggle.com click-through rate (CTR) prediction competition, observe what the winning entries teach about this part of the machine learning landscape, and then discuss the valuable opportunities and resources this commends to AI educators and their students. [excerpt]