Lesson plan
Offline learning
Offline learning is a machine learning training approach in which a model is trained on a fixed dataset that is not updated during the learning process. This dataset is collected beforehand, and the learning typically occurs in a batch mode (i.e., the model is updated using batches of data, rather than a single input-output pair at a time). Once the model is trained, it can make predictions on new, unseen data. In online learning, only the set of possible elements is known, whereas in offline learning, the learner also knows the order in which they are presented.
Article summary
Offline learning is a machine learning training approach in which a model is trained on a fixed dataset that is not updated during the learning process. This dataset is collected beforehand, and the learning typically occurs in a batch mode (i.e., the model is updated using batches of data, rather than a single input-output pair at a time). Once the model is trained, it can make predictions on new, unseen data. In online learning, only the set of possible elements is known, whereas in offline learning, the learner also knows the order in which they are presented.
Key claims
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Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
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Key claims and bibliography are generated from the same tracked claims and sources as the article page.