Explain ML statistics and model design concepts

Question

What is a moment generating function and how is it used? Compare exponential and Poisson distributions. Explain statistical distance and statistical difference. Describe the steps of a hypothesis test. State and explain the Law of Large Numbers. What common assumptions underlie machine-learning models? How does multicollinearity affect Random Forests versus XGBoost? How does number of trees impact performance? Define overfitting and methods to prevent it. Name deep-learning models you are familiar with. Compare RNN and LSTM; explain RNN drawbacks and LSTM structure. What is an autoencoder and its use cases? How does the attention mechanism work? Describe the architecture of a CNN. Explain basic principles of causal inference. Design an ML system to identify relevant users.