1. What is the difference between supervised and unsupervised learning, and when would you use each approach?
2. Can you explain the mathematical underpinnings of a neural network and how it works in practice?
3. What is the curse of dimensionality and how does it affect machine learning algorithms?
4. How do you choose the appropriate evaluation metric for a machine learning model, and why is this important?
5. What is the role of regularization in machine learning, and what are some common methods of regularization?
6. Can you explain the difference between parametric and non-parametric models, and when would you use each approach?
7. What is the bias-variance tradeoff, and how does it impact model performance?
8. How do you deal with missing data in a machine learning dataset, and what are some common imputation methods?
9. Can you explain the concept of cross-validation and how it can be used to evaluate model performance?
10. What are some common approaches to feature selection and extraction in machine learning, and when would you use each approach?