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."

8,208 machine learning interview questions shared by candidates

Reverse the string Several ML questions - what is bias and variance, what is the difference between classification and regression, What is the effect on the output feature maps of the convolution layer that follows a max pooling layer. Why do we want a maxpooling layer. When to use a confusion matrix. What is the difference between precision, recall and F1. what is focal loss, what is dice loss. ML System Design (Facial recognition system) - think about clustering, dimensionality reduction, database storage, approximate nearest neighbor search, reducing database lookup time with horizontal scaling. Also think about feature/concept drift detection and rectification. Talk briefly about knowledge distillation, model quantization and pruning. Data engineering question - how would you remove duplicate images? Hash the images using a fingerprinting algorithm using DFT. Similar to how shazam's audio fingerprinting works. Now you have reduced the problem to removing duplicate fingerprints. If you type "image hashing" in google, a lot of academic papers with noise robust algorithms will pop up.
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Machine Learning Engineer

Interviewed at Carnegie Mellon University

4.4
Jul 18, 2022

Reverse the string Several ML questions - what is bias and variance, what is the difference between classification and regression, What is the effect on the output feature maps of the convolution layer that follows a max pooling layer. Why do we want a maxpooling layer. When to use a confusion matrix. What is the difference between precision, recall and F1. what is focal loss, what is dice loss. ML System Design (Facial recognition system) - think about clustering, dimensionality reduction, database storage, approximate nearest neighbor search, reducing database lookup time with horizontal scaling. Also think about feature/concept drift detection and rectification. Talk briefly about knowledge distillation, model quantization and pruning. Data engineering question - how would you remove duplicate images? Hash the images using a fingerprinting algorithm using DFT. Similar to how shazam's audio fingerprinting works. Now you have reduced the problem to removing duplicate fingerprints. If you type "image hashing" in google, a lot of academic papers with noise robust algorithms will pop up.

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