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THE AORIST AS A RESOURCE
FOR MACHINE LEARNING
EDUCATION
AN EXAMPLE FROM THE ARMENIAN LANGUAGE
MOTIVATION:
For language skills in a languages such as Armenian
to be able to be a machine learning (ML) skill.
The Aorist:
: an inflectional form of a verb
typically denoting simple
occurrence of an action without
reference to its completeness,
duration, or repetition
Source: Merriam-Webster
Dictionary
THE AORIST IS GENERALLY AN ACTION
IN THE PAST THAT IS COMPLETED.
In an earlier time, many
languages around the world
had what is called the aortic.
The aortic is much like the
preterit in English.
Knowledge of the aorist is presented here as a way…
to introduce machine learning(ML).
Although not part of this presentation,
familiarity with the aorist can also assist in learning
aoristic analysis as well Aorist, an ML data management
tool.
An example of machine learning:
Audiences stream episodes of
their favorite shows daily,
then one day there is a spike in
viewership of special.
This explains the aorist in general:
The audience viewed their favorite shows daily,
and the holiday special was viewed with the largest streaming video
audience.
(aorist)
• Much of the work in Machine Learning is cleaning data,
the spike in data would likely be an error.
Especially in Armenian,
the aorist is applied based upon the context.
To determine if a pattern in a chart is a random spike,
context is also needed.
At this point, a theoretical method in
machine learning can be presented.
What is the likelihood of an event
changes based on context?
An application of context can be
found in machine learning in terms of
what is called Bayesian Inference.
Essentially what this is is the
likelihood of an event taking place when
the context of the data changes.
Although beyond the scope of this presentation to
provide a detailed quantitative analysis of Bayesian
Inference,
the next slide is the equation as a reference
and to hopefully encourage further learning of this
topic:
H is for hypothesis E is for evidence
P(H) is for the prior probability
P(H|E) is for the posterior probability
P(E|H) is the likelihood
P(E) is for the marginal likelihood
P(H|E) =
P(E|H) multiplied by P(H)
P(H)

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The Aorist as a Resource for Machine Learning Education

  • 1. THE AORIST AS A RESOURCE FOR MACHINE LEARNING EDUCATION AN EXAMPLE FROM THE ARMENIAN LANGUAGE
  • 2. MOTIVATION: For language skills in a languages such as Armenian to be able to be a machine learning (ML) skill.
  • 3. The Aorist: : an inflectional form of a verb typically denoting simple occurrence of an action without reference to its completeness, duration, or repetition Source: Merriam-Webster Dictionary
  • 4. THE AORIST IS GENERALLY AN ACTION IN THE PAST THAT IS COMPLETED.
  • 5. In an earlier time, many languages around the world had what is called the aortic. The aortic is much like the preterit in English.
  • 6. Knowledge of the aorist is presented here as a way… to introduce machine learning(ML). Although not part of this presentation, familiarity with the aorist can also assist in learning aoristic analysis as well Aorist, an ML data management tool.
  • 7. An example of machine learning: Audiences stream episodes of their favorite shows daily, then one day there is a spike in viewership of special.
  • 8. This explains the aorist in general: The audience viewed their favorite shows daily, and the holiday special was viewed with the largest streaming video audience. (aorist)
  • 9. • Much of the work in Machine Learning is cleaning data, the spike in data would likely be an error.
  • 10. Especially in Armenian, the aorist is applied based upon the context. To determine if a pattern in a chart is a random spike, context is also needed.
  • 11. At this point, a theoretical method in machine learning can be presented. What is the likelihood of an event changes based on context?
  • 12. An application of context can be found in machine learning in terms of what is called Bayesian Inference. Essentially what this is is the likelihood of an event taking place when the context of the data changes.
  • 13. Although beyond the scope of this presentation to provide a detailed quantitative analysis of Bayesian Inference, the next slide is the equation as a reference and to hopefully encourage further learning of this topic:
  • 14. H is for hypothesis E is for evidence P(H) is for the prior probability P(H|E) is for the posterior probability P(E|H) is the likelihood P(E) is for the marginal likelihood P(H|E) = P(E|H) multiplied by P(H) P(H)