You are asked several short ML fundamentals questions: 1) Define **precision** and **recall** for a binary classifier and explain how they relate to a confusion matrix. 2) Given a confusion matrix (TP, FP, TN, FN), compute precision and recall and explain what types of errors increase/decrease each metric. 3) Answer basic questions about **gradients** (e.g., what a gradient represents, and how it is used in gradient descent). 4) Discuss common **ensemble learning tradeoffs** (e.g., bagging vs boosting, bias–variance, compute/latency). 5) Do a simple **neural network forward pass by hand** for a small feed-forward network (matrix multiply + bias + activation) and produce the final output.