CS 540 Fall 2025 Midterm

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Question 1 (MLE)

Question 2 (empirical expectation)

Question 3 (Bayesian inference)

Question 4 (first principal component)

Question 5 (propositional logic)

Question 6 (PCA projection)

Question 7 (bigram MLE)

Question 8 (KNN, Manhattan distance)

Question 9 (learning paradigms — NOT true)

Question 10 (regression vs. classification)

Question 11 (unsupervised goal)

Question 12 (complete-linkage clustering)

Question 13 (k-means assignment, Euclidean)

Question 14 (supervised learning — TRUE)

Question 15 (k-means objective)

Question 16 (gradient descent step for linear regression)

Question 17 (KNN — NOT true)

Question 18 (naive Bayes — NOT true)

Question 19 (output layer for multi-class)

Question 20 (computing gradients)

Question 21 (not unsupervised)

Question 22 (vanishing gradients)

Question 23 (BCE gradient at z=2)

Question 24 (counting parameters)

Question 25 (need for nonlinearity)