Q11: (3 points) Answer True or False only: Reinforcement learning is to model sequential decision data. One of the most famous methods of policy gradient is DQN.

Q22(20 points): Let us consider a one-dimensional space. We wish to perform a hierarchical clustering of the points 2, 6, 10, 14, and 18. Show what happens at each step until there are two clusters and give these two clusters. Your answer should be a table with a row for each step; the row should contain: The members of the new cluster formed  (5 points). Its centroid  (5 points). If you are merging a cluster C1 = {x, y, z} with centroid c1, and a cluster C2 = {p, q} with centroid c2, use the centroid–centroid distance |c2 – c1| to measure the distance between C1 and C2. For the merged cluster, you should report both: The members of the new cluster (5 points). The centroid obtained after merging (5 points).  

Q23 (6points) In an online shopping application scenario, you want to design a recommendation system for complementary or bundle item recommendations (e.g., laptop + laptop bag, pasta + pasta sauce). Which of the following would be a better recommendation system? Choose one and explain the reason(2 points). a) User-user collaborative filteringb) Item-item collaborative filteringc) Content-based recommendation  In one sentence, justify your answer (4 points).

Q3: (3 points) Answer True or False only: In AdaBoosting, subsequent decision stumps are trained by reweighing misclassified samples of the previous decision stumps. In this way, weak learners are combined parallelly to create a strong learner.

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. After updating the weights of the eight samples, the new weights of the eight samples are. Which data instances (samples) is/are correctly classified by the decision stump of this iteration?

Q3: (3 points) Answer True or False only: In AdaBoosting, subsequent decision stumps are trained by reweighing misclassified samples of the previous decision stumps. In this way, weak learners are combined parallelly to create a strong learner.

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

Q11: (3 points) Answer True or False only: Reinforcement learning is to model sequential decision data. One of the most famous methods of policy gradient is DQN.

Q5: (3 points) Answer True or False only: In Boosting, the performance of the final model heavily depends on the performance of the individual weak learners.

Q18: (3 points)List the names and formulas of at least two types of SVM kernels.