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In what ways are supervised, unsupervised, and reinforcement learning distinct in their approach?

When have you been proved wrong

If you took a risk, tell me about it.

What's your understanding of the dissimilarities between the frequentist and Bayesian methods for probability? Further, what benchmarks would you use when selecting which path to take in a given situation?

Could you give an overview of how collaborative filtering is used in ML applications?

Please share an experience where you received negative feedback?

What issues might you face when training deep neural networks that have a lot of layers?

Can you explain the underlying assumptions of linear regression, and the importance of taking them into account when interpreting model outcomes?

What was your most innovative idea?

What steps would you take to test and validate a metric designed to compare two user's Netflix recommended lists of movie/tv show preferences?