The nurse volunteering in the medical tent for a road race on a hot, humid day is asked to see a runner who has collapsed on the road. The nurse notes he has sunken eyes, a temperature of 100°F (37.7°C), and dizziness. These are signs of a fluid volume deficit. Recognizing fluid volume deficit, which of these interventions does the nurse carry out first?

Q16: (4 points)What is the two main objective of a Support Vector Machine?

Q14: (4 points)What is the strategy of AdaBoosting for reweighing the training data points?

Q33: (8 points)After taking the action suggested in the previous question, suppose the discount factor is γ = 0.9, the state transfers from s₂ to s₄ after taking action aₜ, and the reward r is 0.6. Please update the Q-table and write down the updated Q-table. Note: Only one value in the table needs updating, and you might need the Bellman Equation:

Q13: (3 points)In AdaBoosting, there are 8 data instances (i.e., samples). Before updating the weights of the eight samples, the weights of the eight samples are[sample1: 1/8, sample2: 1/8, sample3: 1/8, sample4: 1/8, sample5: 1/8, sample6: 1/8, sample7: 1/8, sample8: 1/8]. After updating the weights of the eight samples, the new weights of the eight samples are[sample1: 0.07, sample2: 0.07, sample3: 0.51, sample4: 0.07, sample5: 0.07, sample6: 0.07, sample7: 0.07, sample8: 0.07]. Which data instances (samples) is/are correctly classified by the decision stump of this iteration?

Q34: (15 points) What are the two key steps of value learning? (4 points) What are the two key steps of policy gradient? (4 points) If we are to build a reinforcement learning with discrete actions, which method we should use? (2 points) What are the weaknesses of value learning (e.g., DQN)? (5 points)

Q28: (6 points)What are the three loss function names of RankSVM, RankBoost, and RankNet discussed in the lectures?

Q25: (10 points) Item-Item Collaborative FilteringBelow is user-movie rating matrix with partial ratings available. Please use the item-item collaborative filtering method to estimate the rating of the user #5 for the movie #1. Hints: first use Pearson correlation as similarity by: subtracting mean rating from each movie, then calculating cosine similarities (Sij) between rows. Later, we predict the rating by taking weighted average using the equation: rix=∑j∈N(i;x)Sij⋅rjx∑Sijr_{ix} = \frac{\sum_{j \in N(i;x)} S_{ij} \cdot r_{jx}}{\sum S_{ij}}​​ where i is the index of an item, x is the index of a user, rjxr_{jx} is the rating of the user x for the item j, N(i;x)N(i;x) is the selected neighbor set of the item i given the user x, and the number of the neighbor set (∣N(i;x)∣|N(i;x)|) is 2.

Q12: (3 points) Answer True or False only: In DQN, the Q value function is instantiated by a neural network.

Evaluate the role of the United Nations in global geopolitics post-World War II. What challenges has it faced in its mission to promote international cooperation and peace?