Explain Core ML Interview Concepts
Company: Amazon
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: hard
Interview Round: Technical Screen
Answer the following machine learning fundamentals questions in a phone screen for an applied scientist role:
1. What are the main assumptions of linear regression? Why is squared loss commonly used?
2. What is logistic regression? Why do logarithms appear in its formulation or loss function?
3. What is a random forest? During tree construction, how is the set of candidate features selected?
4. Explain the Adam optimizer. What are its advantages and disadvantages compared with vanilla stochastic gradient descent?
5. Consider two neural networks with the same two-layer structure. One has only a few neurons per layer, while the other has many neurons per layer. Which one is more likely to get trapped in a poor local minimum, and why?
Quick Answer: This question evaluates core machine learning fundamentals including statistical modeling assumptions and loss functions (linear and logistic regression), ensemble methods and feature sampling in random forests, optimization algorithms (Adam versus stochastic gradient descent), and neural network capacity and training dynamics.
