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Now, from the standpoint of doing applied ML, the ability to solve problems without human labeling effort is huge. So unsupervised learning is a practical concept, but one that is theoretically irrelevant. It affects how you use ML, while being orthogonal to ML algorithms.
The important questions, from the applied ML angle, are: 1. What problems can you solve using data reconstruction as your objective? 2. What automatically-generated or passively-collected alternative to human-provided labels can you use to solve task X?
These are broadly "data science" questions as opposed to algorithmic questions.
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