Machine learning systems do things like mistake a woman’s hair for a cat or think that the name of a band from Montreal could be part of a familiar saying because

C​orrect! The amount of training data impacts the accuracy of the system.

C​orrect! The kinds of answers a QuAM gives depend heavily on the learning algorithm used to build them.

C​orrect! Features capture signal from the raw data, and are used by the QuAM to perform its task. One function of features can be to filter out irrelevant information.

 

Correct! Running a QuAM on live data to verify performance against the existing system can be useful.

Correct, some applications may require models to be human-interpretable.

 

Correct! You should consider what level of accuracy you need for your QuAM to be useful in operation.

C​orrect! You need to consider how use of the QuAM will change the operating environment.

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