Statistics vs. Machine Learning Dictionary (flat text vs. topic map)

Data science terminology (UBC Master of Data Science)

From the webpage:

About this document

This document is intended to help students navigate the large amount of jargon, terminology, and acronyms encountered in the MDS program and beyond. There is also an accompanying blog post.

Stat-ML dictionary

This section covers terms that have different meanings in different contexts, specifically statistics vs. machine learning (ML).
… (emphasis in original)

Gasp! You don’t mean that the same words have different meanings in machine learning and statistics!

Even more shocking, some words/acronyms, have the same meaning!

Never fear, a human reader can use this document to distinguish the usages.

Automated processors, not so much.

If these terms were treated as occurrences of topics, where the topics had the respective scopes of statistics and machine-learning, then for any scoped document, an enhanced view with the correct definition for the unsteady reader could be supplied.

Static markup of legacy documents is not required as annotations can be added as a document is streamed to a reader. Opening the potential, of course, for different annotations depending upon the skill and interest of the reader.

If for each term/subject, more properties than the scope of statistics or machine-learning or both were supplied, users of the topic map could search on those properties to match terms not included here. Such as which type of bias (in statistics) does bias mean in your paper? A casually written Wikipedia article reports twelve and with refinement, the number could be higher.

Flat text is far easier to write than a topic map but tasks every reader with re-discovering the distinctions already known to the author of the document.

Imagine your office, department, agency’s vocabulary and its definitions captured and then used to annotate internal or external documentation for your staff.

Instead of very new staffer asking (hopefully), what do we mean by (your common term), the definition appears with a mouse-over in a document.

Are you capturing the soft knowledge of your staff?

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