Question: In one to three sentences, explain why a learning algorithm can overfit to repeated car noise in synthetic data even though a human listener probably cannot detect the repetition.

Sample answer: A learning algorithm can detect subtle acoustic patterns in the repeated noise clip that are imperceptible to humans, since car noise sounds similar to most people. Because the same hour of noise appears throughout the training data, the algorithm can learn to associate those specific patterns with the training examples rather than learning to handle car noise in general, causing it to generalize poorly to new car noise.

Key points:

• Algorithms can pick up on subtle patterns in noise that humans cannot perceive
• Repetition of the same noise clip lets the algorithm key on its specific characteristics
• This leads to poor generalization on new, differently-sounding car noise

Rubric: Full credit notes that algorithms can detect fine-grained patterns humans cannot, and that repetition allows the algorithm to key on those patterns instead of generalizing. Partial credit for a vaguer statement that the algorithm 'memorizes' the noise.