Q2: (4 points)What is the definition of a support vector?

The CPIs for 2023 and 2024 were respectively 105 and 110. What is the inflation for 2025 if the CPI for that year  is  115. Consider 2024 to be the base year.  

Which of the following is true about nominal GDP? 

  Refer to Figure 1.7. Which of the following set of points all represent inefficient levels of production, ceteris paribus?

Q7: (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:

Q5: (6 points)Give a scenario that hard margin SVM doesn’t work well, and we need soft margin SVMs.

Q2: (6 points) Assuming we aim to build a more advanced recommendation system for an online bookstore using matrix factorization-based methods, similar to the one that won the Netflix prize. Suppose the global mean rating of books is 3.6 stars. Bob, a loyal customer, has rated 400 books, and his average rating is 0.3 stars higher than the global average rating. Meanwhile, Pride and Prejudice is a book in the bookstore that has 200,000 ratings, with an average rating that is 0.5 stars lower than the global average. What would be a baseline estimate of Bob’s rating for Pride and Prejudice? (2 points) Illustrate how you arrived at your answer. (2 points)

Q6: (4 points)What is the strategy of AdaBoosting for reweighing the training data points?

Q3: (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.

Q4: (8 points) Designing A Machine Learning System.Given user features, item features, and a user-item-rating matrix, if we formulate the problem of recommending personalized items for users as a ranking task, how can we use develop a personalized Learning To Rank (LTR) model for recommendations? Please specify: how you will use the data what is your model structure what is your objective function how to use the learned ranking model to conduct personalized recommendations.