According to Tom Mitchell's definition of machine learning as explained in Kevin Murphy's Probabilistic Machine Learning book, what are the three key components that determine the type of machine learning?

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BlackTom AI · Accepted Answer

The question asks about Tom Mitchell's standard framing of machine learning as described in Kevin Murphy's Probabilistic ML text, specifically the three elements that define the type of learning.
First, consider what each element represents in Mitchell’s definition: the Task is what the learner is trying to accomplish, the Performance Measure is how we judge how well the learner does on that task, and the Experience refers to the data or past interactions the learner uses to improve.
If we look at the proposed trio, Task, Performance Measure, and Experience, it aligns precisely with Mitchell’s classic schema: the task sets the goal, the performance measure provides the criterion for success, and the experience supplies data from which the model learns.
Regarding potential alternative options, a common pitfall is to swap one of these components for an unrelated concept (for example, confusing the learning algorithm with the data labeling process or with evaluation metrics alone). However, those misunderstandings miss one of the core pillars or conflate distinct ideas, and they would not preserve the clean triplet that Mitchell specifies.
Additionally, note that the three components are interdependent: the choice of task informs what constitutes a good performance, the performance measure depends on the task, and the experience must be relevant to the task and aligned with the performance criterion to enable learning.
In summary, identifying Task, Performance Measure, and Experience as the three key components correctly captures Mitchell’s framework for categorizing types of learning in machine learning, reflecting how the goal, the success criterion, and the data jointly define the learning problem.

CS7646 Course Content Quiz #2

According to Tom Mitchell's definition of machine learning as explained in Kevin Murphy's Probabilistic Machine Learning book, what are the three key components that determine the type of machine learning?

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