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Machine Learning Scientist Interview Questions
467 machine learning scientist interview questions shared by candidates
Tell me about a model of time series data.
Q: What is the meaning of "overfitting"?
What is cross sectional analysis and when should you use it?
You have a partially observable environment with evolving dynamics (non-stationary transition and reward distributions). Logged data comes from multiple behavior policies. How would you estimate the expected return of a new policy and safely improve it, without deploying it, while accounting for uncertainty in both the dynamics and the behavior policies?
Q: Talk about why you would want to use a specific model.
Q: You are the ML Engineer who developed a recommendation system model. The model goes into production and behaves not normally, like discrimination or other things. What are you going to do?
What is your interest region?
You will be asked a wide range of ML-related questions (ML theory, PyTorch, CNNs, etc.). You will also be asked to code towards the end of the 1 hour session (Leetcode medium). Most of these questions have well-defined answers (e.g., how do you disable gradient computation in PyTorch) while others are more open-ended (e.g., how would you use unlabeled data to boost the performance of your supervised tasks). My major complaints are with these open-ended questions. The interviewer had specific answers in mind and would not understand/accept alternative approaches. The depth of the interviewer's ML knowledge is also questionable as the interviewer did not understand how pretrained networks can be used as feature extractors. The interviewer also asked about variational auto-encoder without knowing the underlying probabilistic formulation. Overall, a negative experience.
Writing loops in Python or other languages
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