Machine Learning Interviews

Machine Learning Interview Questions

"To get a job in machine learning, you must have the programming and mathematical knowledge to create artificial intelligence that is capable of learning new tasks without being explicitly coded. In an interview you may be asked about your experience with pertinent coding languages such as Java and C++ as well as with writing algorithms. The interview will be comprised mainly of technical questions that test your knowledge of the fundamental concepts of machine learning such as data mining and signal processing."

8,212 machine learning interview questions shared by candidates

Describe a VAE in detail Describe Attention and Seq-2-Seq modelling in detail Describe how you would combine the two above to obtain a text to speech generation pipeline Describe Neural Processes paper in detail. Questions about Stochastic Processes and benefit of moving to neural processes. ELBO derivations/summary. How to align text sequence to sequence of phonemes (different lengths)
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Machine Learning Engineer

Interviewed at Papercup.AI

4.2
Sep 30, 2021

Describe a VAE in detail Describe Attention and Seq-2-Seq modelling in detail Describe how you would combine the two above to obtain a text to speech generation pipeline Describe Neural Processes paper in detail. Questions about Stochastic Processes and benefit of moving to neural processes. ELBO derivations/summary. How to align text sequence to sequence of phonemes (different lengths)

We spent some short time in discussing resume projects. But then we spent the rest of time discussing a single ML model in depth. From data formulation, processing to how the model works to regularization methods. So I say you have to at least review the models to a complete graphic level and shallow-ish math equation level in order to answer the questions. (BTW the interviewer will expect a "correct" answer) How Linear regression works? How are the weights updated? How by adding a regularization term can reduce overfitting? (Think mathematically) And questions like how to deal with imbalanced labels? (I provided 2 solutions but the interviewer was expecting something else, and honestly we don't learn that in graduate school...)
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Machine Learning Intern

Interviewed at Infrrd

3.8
Sep 8, 2020

We spent some short time in discussing resume projects. But then we spent the rest of time discussing a single ML model in depth. From data formulation, processing to how the model works to regularization methods. So I say you have to at least review the models to a complete graphic level and shallow-ish math equation level in order to answer the questions. (BTW the interviewer will expect a "correct" answer) How Linear regression works? How are the weights updated? How by adding a regularization term can reduce overfitting? (Think mathematically) And questions like how to deal with imbalanced labels? (I provided 2 solutions but the interviewer was expecting something else, and honestly we don't learn that in graduate school...)

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