CS 540 Spring 2025 Final

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Question 1 (conv output size)

Question 2 (CNN advantages)

Question 3 (nonlinearity in a CNN)

Question 4 (CNN layer features)

Question 5 (counting conv parameters)

Question 6 (vanishing gradient fix)

Question 7 (residual networks)

Question 8 (overfitting — NOT a fix)

Question 9 (BFS vs. DFS goal)

Question 10 (UCS data structure)

Question 11 (preserving admissibility)

Question 12 (complete and optimal)

Question 13 (flower-graph fringe sizes)

Question 14 (strict dominance)

Question 15 (counting minimax-tree nodes)

Question 16 (Markov assumption)

Question 17 (Q-learning with ε-greedy)

Question 18 (one value-iteration step)

Question 19 (generalization)

Question 20 (simulated annealing, low temperature)

Question 21 (optimal warehouse route)

Question 22 (hill climbing vs. SGD)

Question 23 (reducing bias / increasing trust)

Question 24 (training neural networks)

Question 25 (MLE for a coin)

Question 26 (least-squares weights)

Question 27 (one step of 2-means)

Question 28 (cross-entropy loss)

Question 29 (gradient descent facts)

Question 30 (Nash equilibrium)